Welcome to Copenhagen!

Transcription

1 Welcome to Copenhagen! Schedule: Monday Tuesday Wednesday Thursday Friday 8 Registration and welcome 9 Crash course on Crash course on Introduction to Differential and Differential and Information Geometry & Information Geometry & Information Geometry Riemannian Riemannian Stochastic Optimization Stochastic Optimization 3.1 Geometry 1.1 Geometry in Discrete Domains 1.1 (Amari) (Feragen) (Lauze) (Hansen) (Màlago) 10 Crash course on Differential and Riemannian Geometry 1.2 (Feragen) Tutorial on numerics for Riemannian geometry 1.1 (Sommer) Introduction to Information Geometry & Information Geometry & Information Geometry Stochastic Optimization Stochastic Optimization in Discrete Domains 1.2 (Amari) (Hansen) (Màlago) 11 Crash course on Differential and Riemannian Geometry 1.3 (Feragen) Tutorial on numerics for Riemannian geometry 1.2 (Sommer) Introduction to Information Geometry & Information Geometry & Information Geometry Stochastic Optimization Stochastic Optimization in Discrete Domains 1.3 (Amari) (Hansen) (Màlago) 12 Lunch Lunch Lunch Lunch Lunch 13 Crash course on Differential and Introduction to Information Geometry & Information Geometry & Information Geometry Information Geometry & Riemannian Reinforcement Learning Stochastic Optimization 2.1 Cognitive Systems 1.1 Geometry (Amari) (Ay) (Lauze) (Peters) (Hansen) 14 Crash course on Differential and Introduction to Information Geometry & Information Geometry & Information Geometry Information Geometry & Riemannian Reinforcement Learning Stochastic Optimization 2.2 Cognitive Systems 1.2 Geometry (Amari) (Ay) (Lauze) (Peters) (Hansen) 15 Introduction to Introduction to Information Geometry & Stochastic Optimization Information Geometry Information Geometry Information Geometry & Reinforcement Learning in Practice Cognitive Systems (Hansen) (Amari) (Amari) (Ay) (Peters) 16 Introduction to Introduction to Information Geometry Information Geometry Social activity/ Stochastic Optimization Information Geometry & Networking event in Practice 1.2 Cognitive Systems 1.3 (Hansen) (Amari) (Amari) (Ay) Coffee breaks at 10:00 and 14:45 (no afternoon break on Wednesday) Slide 1/57 Aasa Feragen and François Lauze Differential Geometry September 22

2 Welcome to Copenhagen! Social Programme! Today: Pizza and walking tour! 17:15 Pizza dinner in lecture hall 18:00 Departure from lecture hall (with Metro we have tickets) 19:00 Walking tour of old university Wednesday: Boat tour, Danish beer and dinner 15:20 Bus from KUA to Nyhavn 16:00-17:00 Boat tour 17:20 Bus from Nyhavn to NÃ rrebro bryghus (NB, brewery) 18:00 Guided tour of NB 19:00 Dinner at NB Slide 1/57 Aasa Feragen and François Lauze Differential Geometry September 22

3 Welcome to Copenhagen! Lunch on your own canteens and coffee on campus Internet connection Eduroam Alternative will be set up ASAP Emergency? Call Aasa: Questions? Slide 1/57 Aasa Feragen and François Lauze Differential Geometry September 22

4 UNIVERSITY OF COPENHAGEN UNIVERSITY OF COPENHAGEN Faculty of Science A Very Brief Introduction to Differential and Riemannian Geometry Aasa Feragen and François Lauze Department of Computer Science University of Copenhagen PhD course on Information geometry, Copenhagen 2014 Slide 2/57

5 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 3/57 Aasa Feragen and François Lauze Differential Geometry September 22

6 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 4/57 Aasa Feragen and François Lauze Differential Geometry September 22

7 Why do we care about nonlinearity? Nonlinear relations between data objects True distances not reflected by linear representation Topographic map example. Licensed under Public domain via Wikimedia Commons - Topographic_map_example.png Slide 5/57 Aasa Feragen and François Lauze Differential Geometry September 22

8 Mildly nonlinear: Nonlinear transformations between different linear representations Kernels! Feature map = nonlinear transformation of (linear?) data space X into linear feature space H Learning problem is (usually) linear in H, not in X. Slide 6/57 Aasa Feragen and François Lauze Differential Geometry September 22

9 Mildly nonlinear: Nonlinearly embedded subspaces whose intrinsic metric is linear Manifold learning! Find intrinsic dataset distances Find an R d embedding that minimally distorts those distances Slide 7/57 Aasa Feragen and François Lauze Differential Geometry September 22

10 Mildly nonlinear: Nonlinearly embedded subspaces whose intrinsic metric is linear Manifold learning! Find intrinsic dataset distances Find an R d embedding that minimally distorts those distances Searches for the folded-up Euclidean space that best fits the data the embedding of the data in feature space is nonlinear the recovered intrinsic distance structure is linear Slide 7/57 Aasa Feragen and François Lauze Differential Geometry September 22

11 More nonlinear: Data spaces which are intrinsically nonlinear Distances distorted in nonlinear way, varying spatially We shall see: the distances cannot always be linearized Topographic map example. Licensed under Public domain via Wikimedia Commons - Topographic_map_example.png Slide 8/57 Aasa Feragen and François Lauze Differential Geometry September 22

12 Intrinsically nonlinear data spaces: Smooth manifolds Definition A manifold is a set M with an associated one-to-one map ϕ: U M from an open subset U R m called a global chart or a global coordinate system for M. Slide 9/57 Aasa Feragen and François Lauze Differential Geometry September 22

13 Intrinsically nonlinear data spaces: Smooth manifolds Definition A manifold is a set M with an associated one-to-one map ϕ: U M from an open subset U R m called a global chart or a global coordinate system for M. Open set U R m = set that does not contain its boundary Manifold M gets its topology (= definition of open sets) from U via ϕ Slide 9/57 Aasa Feragen and François Lauze Differential Geometry September 22

14 Intrinsically nonlinear data spaces: Smooth manifolds Definition A manifold is a set M with an associated one-to-one map ϕ: U M from an open subset U R m called a global chart or a global coordinate system for M. Open set U R m = set that does not contain its boundary Manifold M gets its topology (= definition of open sets) from U via ϕ What are the implications of getting the topology from U? Slide 9/57 Aasa Feragen and François Lauze Differential Geometry September 22

15 Intrinsically nonlinear data spaces: Smooth manifolds Definition A smooth manifold is a pair (M, A) where M is a set A is a family of one-to-one global charts ϕ: U M from some open subset U = U ϕ R m for M, for any two charts ϕ: U R m and ψ : V R m in A, their corresponding change of variables is a smooth diffeomorphism ψ 1 ϕ: U V R m. Slide 9/57 Aasa Feragen and François Lauze Differential Geometry September 22

16 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 10/57 Aasa Feragen and François Lauze Differential Geometry September 22

17 Differentiable and smooth functions f : U open R n R q continuous: write (y 1,..., y q ) = f (x 1,..., x n ) Slide 11/57 Aasa Feragen and François Lauze Differential Geometry September 22

18 Differentiable and smooth functions f : U open R n R q continuous: write (y 1,..., y q ) = f (x 1,..., x n ) f is of class C r if f has continuous partial derivatives k = 1... q, r r n r. r 1+ +r n y k x r 1 rn 1... xn Slide 11/57 Aasa Feragen and François Lauze Differential Geometry September 22

19 Differentiable and smooth functions f : U open R n R q continuous: write (y 1,..., y q ) = f (x 1,..., x n ) f is of class C r if f has continuous partial derivatives k = 1... q, r r n r. r 1+ +r n y k x r 1 rn 1... xn When r =, f is smooth. Our focus. Slide 11/57 Aasa Feragen and François Lauze Differential Geometry September 22

20 Differential, Jacobian Matrix Differential of f in x: unique linear map (if exists) d x f : R n R q s.t. f (x + h) = f (x) + d x f (h) + o(h). Slide 12/57 Aasa Feragen and François Lauze Differential Geometry September 22

21 Differential, Jacobian Matrix Differential of f in x: unique linear map (if exists) d x f : R n R q s.t. f (x + h) = f (x) + d x f (h) + o(h). Jacobian matrix of f : matrix q n of partial derivatives of f : y 1 y x (x) x (x) n J x f =.. y q y x (x) 1... q x (x) n Slide 12/57 Aasa Feragen and François Lauze Differential Geometry September 22

22 Differential, Jacobian Matrix Differential of f in x: unique linear map (if exists) d x f : R n R q s.t. f (x + h) = f (x) + d x f (h) + o(h). Jacobian matrix of f : matrix q n of partial derivatives of f : y 1 y x (x) x (x) n J x f =.. y q y x (x) 1... q x (x) n What is the meaning of the Jacobian? The differential? How do they differ? Slide 12/57 Aasa Feragen and François Lauze Differential Geometry September 22

23 Diffeomorphism When n = q: If f is 1-1, f and f 1 both C r f is a C r -diffeomorphism. Smooth diffeomorphisms are simply referred to as a diffeomorphisms. Slide 13/57 Aasa Feragen and François Lauze Differential Geometry September 22

24 Diffeomorphism When n = q: If f is 1-1, f and f 1 both C r f is a C r -diffeomorphism. Smooth diffeomorphisms are simply referred to as a diffeomorphisms. Inverse Function Theorem: f diffeomorphism det(j xf ) 0. det(j xf ) 0 f local diffeomorphism in a neighborhood of x. Slide 13/57 Aasa Feragen and François Lauze Differential Geometry September 22

25 Diffeomorphism When n = q: If f is 1-1, f and f 1 both C r f is a C r -diffeomorphism. Smooth diffeomorphisms are simply referred to as a diffeomorphisms. Inverse Function Theorem: f diffeomorphism det(j xf ) 0. det(j xf ) 0 f local diffeomorphism in a neighborhood of x. What is the meaning of J x f? Of det(j x f ) 0? Slide 13/57 Aasa Feragen and François Lauze Differential Geometry September 22

26 Diffeomorphism f may be a local diffeomorphism everywhere but fail to be a global diffeomorphism. Examples: Complex exponential: f : R 2 \0 R 2, (x, y) (e x cos(y), e x sin(y)). Recall its inverse (the complex log) has infinitely many branches. Complex log by Jan Homann; Color encoding image comment author Hal Lane, September 28, Own work. This mathematical image was created with Mathematica. Licensed under Public domain via Wikimedia Commons - Slide 14/57 Aasa Feragen and François Lauze Differential Geometry September 22

27 Diffeomorphism f may be a local diffeomorphism everywhere but fail to be a global diffeomorphism. Examples: Complex exponential: f : R 2 \0 R 2, (x, y) (e x cos(y), e x sin(y)). Recall its inverse (the complex log) has infinitely many branches. If f is 1-1 and a local diffeomorphism everywhere, it is a global diffeomorphism. Complex log by Jan Homann; Color encoding image comment author Hal Lane, September 28, Own work. This mathematical image was created with Mathematica. Licensed under Public domain via Wikimedia Commons - Slide 14/57 Aasa Feragen and François Lauze Differential Geometry September 22

28 Diffeomorphism f may be a local diffeomorphism everywhere but fail to be a global diffeomorphism. Examples: Complex exponential: f : R 2 \0 R 2, (x, y) (e x cos(y), e x sin(y)). Recall its inverse (the complex log) has infinitely many branches. If f is 1-1 and a local diffeomorphism everywhere, it is a global diffeomorphism. What is the intuitive meaning of a diffeomorphism? Complex log by Jan Homann; Color encoding image comment author Hal Lane, September 28, Own work. This mathematical image was created with Mathematica. Licensed under Public domain via Wikimedia Commons - Slide 14/57 Aasa Feragen and François Lauze Differential Geometry September 22

29 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 15/57 Aasa Feragen and François Lauze Differential Geometry September 22

30 Back to smooth manifolds Definition A smooth manifold is a pair (M, A) where M is a set A is a family of one-to-one global charts ϕ: U M from some open subset U = U ϕ R m for M, for any two charts ϕ: U R m and ψ : V R m, their corresponding change of variables is a smooth diffeomorphism ψ 1 ϕ: U V R m. Slide 16/57 Aasa Feragen and François Lauze Differential Geometry September 22

31 Back to smooth manifolds Definition A smooth manifold is a pair (M, A) where M is a set A is a family of one-to-one global charts ϕ: U M from some open subset U = U ϕ R m for M, for any two charts ϕ: U R m and ψ : V R m, their corresponding change of variables is a smooth diffeomorphism ψ 1 ϕ: U V R m. What are the implications of inheriting structure through A? Slide 16/57 Aasa Feragen and François Lauze Differential Geometry September 22

32 Back to smooth manifolds ϕ and ψ are parametrizations of M Slide 16/57 Aasa Feragen and François Lauze Differential Geometry September 22

33 Back to smooth manifolds ϕ and ψ are parametrizations of M Set ϕ j (P) = (y 1 (P),..., y n (P)), then ϕ j ϕ 1 i (x 1,..., x m ) = (y 1,..., y m ) and the m m Jacobian matrices ( y k x )k,h h are invertible. Slide 16/57 Aasa Feragen and François Lauze Differential Geometry September 22

34 Example: Euclidean space The Euclidean space R n is a manifold: take ϕ = Id as global coordinate system! Slide 17/57 Aasa Feragen and François Lauze Differential Geometry September 22

35 Example: Smooth surfaces Smooth surfaces in R n that are the image of a smooth map f : R 2 R n. A global coordinate system given by f Slide 18/57 Aasa Feragen and François Lauze Differential Geometry September 22

36 UNIVERSITY OF COPENHAGEN UNIVERSITY OF COPENHAGEN Example: Symmetric Positive Definite Matrices P(n) GLn consists of all symmetric n n matrices A that satisfy xax T > 0 for any x Rn, (positive definite PD matrices) P(n) = the set of covariance matrices on Rn P(3) = the set of (diffusion) tensors on R3 2 Global chart: P(n) is an open, convex subset of R(n +n)/2 A, B P(n) aa + bb P(n) for all a, b > 0 so P(n) is a convex 2 cone in R(n +n)/2. Middle figure from Fillard et al., A Riemannian Framework for the Processing of Tensor-Valued Images, LNCS 3753, 2005, pp Rightmost figure from Fletcher, Joshi, Principal Geodesic Analysis on Symmetric Spaces: Statistics of Diffusion Tensors, CVAMIA04 Slide 19/57 Aasa Feragen and François Lauze Differential Geometry September 22

37 Example: Space of Gaussian distributions The space of n-dimensional Gaussian distributions is a smooth manifold Global chart: (µ, Σ) R n P(n). Slide 20/57 Aasa Feragen and François Lauze Differential Geometry September 22

38 Example: Space of 1-dimensional Gaussian distributions The space of 1-dimensional Gaussian distributions is parametrized by (µ, σ) R R +, mean µ, standard deviation σ Also parametrized by (µ, σ 2 ) R R +, mean µ, variance σ 2 Smooth reparametrization ψ 1 ϕ Slide 21/57 Aasa Feragen and François Lauze Differential Geometry September 22

39 In general: Manifolds requiring multiple charts The sphere S 2 = {(x, y, z), x 2 + y 2 + z 2 = 1} For instance the projection from North Pole, given, for a point P = (x, y, z) N of the sphere, by ( ) x ϕ N (P) = 1 z, y 1 z is a (large) local coordinate system (around the south pole). Slide 22/57 Aasa Feragen and François Lauze Differential Geometry September 22

40 In general: Manifolds requiring multiple charts The sphere S 2 = {(x, y, z), x 2 + y 2 + z 2 = 1} For instance the projection from North Pole, given, for a point P = (x, y, z) N of the sphere, by ( ) x ϕ N (P) = 1 z, y 1 z is a (large) local coordinate system (around the south pole). In these cases, we also require the charts to overlap nicely Slide 22/57 Aasa Feragen and François Lauze Differential Geometry September 22

41 In general: Manifolds requiring multiple charts The Moebius strip The 2D-torus u [0, 2π], v [ 1 2, 1 2 ] cos(u) ( v cos( u 2 )) sin(u) ( v cos( u 2 )) 1 2 v sin( u 2 ) (u, v) [0, 2π] 2, R r > 0 cos(u) (R + r cos(v)) sin(u) (R + r cos(v)) r sin(v) Slide 22/57 Aasa Feragen and François Lauze Differential Geometry September 22

42 Smooth maps between manifolds f : M N is smooth if its expression in any global coordinates for M and N is. Slide 23/57 Aasa Feragen and François Lauze Differential Geometry September 22

43 Smooth maps between manifolds f : M N is smooth if its expression in any global coordinates for M and N is. ϕ global coordinates for M, ψ global coordinates for N Slide 23/57 Aasa Feragen and François Lauze Differential Geometry September 22

44 Smooth maps between manifolds f : M N is smooth if its expression in any global coordinates for M and N is. ϕ global coordinates for M, ψ global coordinates for N ϕ 1 f ψ smooth. Slide 23/57 Aasa Feragen and François Lauze Differential Geometry September 22

45 Smooth diffeomorphism between manifolds f : M N is a smooth diffeomorphism if its expression in any global coordinates for M and N is. Slide 24/57 Aasa Feragen and François Lauze Differential Geometry September 22

46 Smooth diffeomorphism between manifolds f : M N is a smooth diffeomorphism if its expression in any global coordinates for M and N is. ϕ global coordinates for M, ψ global coordinates for N Slide 24/57 Aasa Feragen and François Lauze Differential Geometry September 22

47 Smooth diffeomorphism between manifolds f : M N is a smooth diffeomorphism if its expression in any global coordinates for M and N is. ϕ global coordinates for M, ψ global coordinates for N ϕ 1 f ψ smooth diffeomorphism. Slide 24/57 Aasa Feragen and François Lauze Differential Geometry September 22

48 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 25/57 Aasa Feragen and François Lauze Differential Geometry September 22

49 Submanifolds of R N Take f : U R m R n, n m smooth. Slide 26/57 Aasa Feragen and François Lauze Differential Geometry September 22

50 Submanifolds of R N Take f : U R m R n, n m smooth. Set M = f 1 (0). Slide 26/57 Aasa Feragen and François Lauze Differential Geometry September 22

51 Submanifolds of R N Take f : U R m R n, n m smooth. Set M = f 1 (0). If for all x M, f is a submersion at x (d x f has full rank), M is a manifold of dimension m n. Slide 26/57 Aasa Feragen and François Lauze Differential Geometry September 22

52 Submanifolds of R N Take f : U R m R n, n m smooth. Set M = f 1 (0). If for all x M, f is a submersion at x (d x f has full rank), M is a manifold of dimension m n. Example: f (x 1,..., x m ) = 1 m i=1 x 2 i : f 1 (0) is the (m 1)-dimensional unit sphere S m 1. Slide 26/57 Aasa Feragen and François Lauze Differential Geometry September 22

53 Submanifolds of R N Take f : U R m R n, n m smooth. Set M = f 1 (0). If for all x M, f is a submersion at x (d x f has full rank), M is a manifold of dimension m n. Example: f (x 1,..., x m ) = 1 m i=1 x 2 i : f 1 (0) is the (m 1)-dimensional unit sphere S m 1. The graph Γ = (x, f (x)) R m R n is smooth for any smooth map f : R m R n. Slide 26/57 Aasa Feragen and François Lauze Differential Geometry September 22

54 Submanifolds of R N Take f : U R m R n, n m smooth. Set M = f 1 (0). If for all x M, f is a submersion at x (d x f has full rank), M is a manifold of dimension m n. Example: f (x 1,..., x m ) = 1 m i=1 x 2 i : f 1 (0) is the (m 1)-dimensional unit sphere S m 1. The graph Γ = (x, f (x)) R m R n is smooth for any smooth map f : R m R n. Γ = F ( 0) for F : R m R n R n, F(x, y) = y f (x). Slide 26/57 Aasa Feragen and François Lauze Differential Geometry September 22

55 Submanifolds of R N Take f : U R m R n, n m smooth. Set M = f 1 (0). If for all x M, f is a submersion at x (d x f has full rank), M is a manifold of dimension m n. Example: f (x 1,..., x m ) = 1 m i=1 x 2 i : f 1 (0) is the (m 1)-dimensional unit sphere S m 1. The graph Γ = (x, f (x)) R m R n is smooth for any smooth map f : R m R n. Γ = F ( 0) for F : R m R n R n, F(x, y) = y f (x). Many common examples of manifolds in practice are of that type. Slide 26/57 Aasa Feragen and François Lauze Differential Geometry September 22

56 Product Manifolds M and N manifolds, so is M N. Slide 27/57 Aasa Feragen and François Lauze Differential Geometry September 22

57 Product Manifolds M and N manifolds, so is M N. Just consider the products of charts of M and N! Slide 27/57 Aasa Feragen and François Lauze Differential Geometry September 22

58 Product Manifolds M and N manifolds, so is M N. Just consider the products of charts of M and N! Example: M = S 1, N = R: the cylinder Slide 27/57 Aasa Feragen and François Lauze Differential Geometry September 22

59 Product Manifolds M and N manifolds, so is M N. Just consider the products of charts of M and N! Example: M = S 1, N = R: the cylinder Example: M = N = S 1 : the torus Slide 27/57 Aasa Feragen and François Lauze Differential Geometry September 22

60 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 28/57 Aasa Feragen and François Lauze Differential Geometry September 22

61 Tangent vectors informally How can we quantify tangent vectors to a manifold? Slide 29/57 Aasa Feragen and François Lauze Differential Geometry September 22

62 Tangent vectors informally How can we quantify tangent vectors to a manifold? Slide 29/57 Aasa Feragen and François Lauze Differential Geometry September 22

63 Tangent vectors informally How can we quantify tangent vectors to a manifold? Informally: a tangent vector at P M: draw a curve c : ( ε, ε) M, c(0) = P, then ċ(0) is a tangent vector. Slide 29/57 Aasa Feragen and François Lauze Differential Geometry September 22

64 A bit more formally. Slide 30/57 Aasa Feragen and François Lauze Differential Geometry September 22

65 A bit more formally c : ( ε, ε) M, c(0) = P. In chart ϕ, the map t ϕ c(t) is a curve in Euclidean space, and so is t ψ c(t).. Slide 30/57 Aasa Feragen and François Lauze Differential Geometry September 22

66 A bit more formally c : ( ε, ε) M, c(0) = P. In chart ϕ, the map t ϕ c(t) is a curve in Euclidean space, and so is t ψ c(t). set v = d dt (ϕ c) 0, w = d dt (ψ c) 0 then (J 0 f = Jacobian of f at 0) w = J 0 ( ϕ 1 ψ ) v.. Slide 30/57 Aasa Feragen and François Lauze Differential Geometry September 22

67 A bit more formally c : ( ε, ε) M, c(0) = P. In chart ϕ, the map t ϕ c(t) is a curve in Euclidean space, and so is t ψ c(t). set v = d dt (ϕ c) 0, w = d dt (ψ c) 0 then w = J 0 ( ϕ 1 ψ ) v. (J 0 f = Jacobian of f at 0) Use this relation to identify vectors in different coordinate systems!. Slide 30/57 Aasa Feragen and François Lauze Differential Geometry September 22

68 Tangent space The set of tangent vectors to the m-dimensional manifold M at point P is the tangent space of M at P denoted T P M. Slide 31/57 Aasa Feragen and François Lauze Differential Geometry September 22

69 Tangent space The set of tangent vectors to the m-dimensional manifold M at point P is the tangent space of M at P denoted T P M. It is a vector space of dimension m: Slide 31/57 Aasa Feragen and François Lauze Differential Geometry September 22

70 Tangent space The set of tangent vectors to the m-dimensional manifold M at point P is the tangent space of M at P denoted T P M. It is a vector space of dimension m: Let ϕ be a global chart for M Slide 31/57 Aasa Feragen and François Lauze Differential Geometry September 22

71 Tangent space The set of tangent vectors to the m-dimensional manifold M at point P is the tangent space of M at P denoted T P M. It is a vector space of dimension m: Let ϕ be a global chart for M ϕ(x 1,..., x m) M with ϕ(0) = P. Slide 31/57 Aasa Feragen and François Lauze Differential Geometry September 22

72 Tangent space The set of tangent vectors to the m-dimensional manifold M at point P is the tangent space of M at P denoted T P M. It is a vector space of dimension m: Let ϕ be a global chart for M ϕ(x 1,..., x m) M with ϕ(0) = P. Define curves c i : t ϕ(0,..., 0, t, 0, 0). Slide 31/57 Aasa Feragen and François Lauze Differential Geometry September 22

73 Tangent space The set of tangent vectors to the m-dimensional manifold M at point P is the tangent space of M at P denoted T P M. It is a vector space of dimension m: Let ϕ be a global chart for M ϕ(x 1,..., x m) M with ϕ(0) = P. Define curves c i : t ϕ(0,..., 0, t, 0, 0). They go through P when t = 0 and follow the axes. Slide 31/57 Aasa Feragen and François Lauze Differential Geometry September 22

74 Tangent space The set of tangent vectors to the m-dimensional manifold M at point P is the tangent space of M at P denoted T P M. It is a vector space of dimension m: Let ϕ be a global chart for M ϕ(x 1,..., x m) M with ϕ(0) = P. Define curves c i : t ϕ(0,..., 0, t, 0, 0). They go through P when t = 0 and follow the axes. Their derivative at 0 are denoted xi, or sometimes x i. Slide 31/57 Aasa Feragen and François Lauze Differential Geometry September 22

75 Tangent space The set of tangent vectors to the m-dimensional manifold M at point P is the tangent space of M at P denoted T P M. It is a vector space of dimension m: Let ϕ be a global chart for M ϕ(x 1,..., x m) M with ϕ(0) = P. Define curves c i : t ϕ(0,..., 0, t, 0, 0). They go through P when t = 0 and follow the axes. Their derivative at 0 are denoted xi, or sometimes x i. The xi form a basis of T P M. Slide 31/57 Aasa Feragen and François Lauze Differential Geometry September 22

76 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 32/57 Aasa Feragen and François Lauze Differential Geometry September 22

77 Vector fields A vector field is a smooth map that sends P M to a vector v(p) T P M. Slide 33/57 Aasa Feragen and François Lauze Differential Geometry September 22

78 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 34/57 Aasa Feragen and François Lauze Differential Geometry September 22

79 Differential of a smooth map f : M N smooth, P M, f (P) N Slide 35/57 Aasa Feragen and François Lauze Differential Geometry September 22

80 Differential of a smooth map f : M N smooth, P M, f (P) N d P f : T P M T f (P) N linear map corresponding to the Jacobian matrix of f in local coordinates. Slide 35/57 Aasa Feragen and François Lauze Differential Geometry September 22

81 Differential of a smooth map f : M N smooth, P M, f (P) N d P f : T P M T f (P) N linear map corresponding to the Jacobian matrix of f in local coordinates. When N = R, d P f is a linear form T P M R. Slide 35/57 Aasa Feragen and François Lauze Differential Geometry September 22

82 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 36/57 Aasa Feragen and François Lauze Differential Geometry September 22

83 Tools needed in intrinsically nonlinear spaces? Comparison of objects in a nonlinear space? Topographic map example. Licensed under Public domain via Wikimedia Commons - Topographic_map_example.png Slide 37/57 Aasa Feragen and François Lauze Differential Geometry September 22

84 Tools needed in intrinsically nonlinear spaces? Comparison of objects in a nonlinear space? Distance metric? Kernel? Varying local inner product = Riemannian metric! Topographic map example. Licensed under Public domain via Wikimedia Commons - Topographic_map_example.png Slide 37/57 Aasa Feragen and François Lauze Differential Geometry September 22

85 Tools needed in intrinsically nonlinear spaces? Comparison of objects in a nonlinear space? Distance metric? Kernel? Varying local inner product = Riemannian metric! Optimization over such spaces? Topographic map example. Licensed under Public domain via Wikimedia Commons - Topographic_map_example.png Slide 37/57 Aasa Feragen and François Lauze Differential Geometry September 22

86 Tools needed in intrinsically nonlinear spaces? Comparison of objects in a nonlinear space? Distance metric? Kernel? Varying local inner product = Riemannian metric! Optimization over such spaces? Gradients! Topographic map example. Licensed under Public domain via Wikimedia Commons - Topographic_map_example.png Slide 37/57 Aasa Feragen and François Lauze Differential Geometry September 22

87 Tools needed in intrinsically nonlinear spaces? Comparison of objects in a nonlinear space? Distance metric? Kernel? Varying local inner product = Riemannian metric! Optimization over such spaces? Gradients! Riemannian geometry Topographic map example. Licensed under Public domain via Wikimedia Commons - Topographic_map_example.png Slide 37/57 Aasa Feragen and François Lauze Differential Geometry September 22

88 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 38/57 Aasa Feragen and François Lauze Differential Geometry September 22

89 Recall: Inner Products Euclidean/Hilbertian Inner Product on vector space E: bilinear, symmetric, positive definite mapping x, y R. Slide 39/57 Aasa Feragen and François Lauze Differential Geometry September 22

90 Recall: Inner Products Euclidean/Hilbertian Inner Product on vector space E: bilinear, symmetric, positive definite mapping x, y R. Simplest example: usual dot-product on R n : x = (x 1,..., x n ) t, y = (y 1,..., y n ) t, x y = x, y = n x i y i = x t Id y i=1 Id n n identity matrix. Slide 39/57 Aasa Feragen and François Lauze Differential Geometry September 22

91 Recall: Inner Products Euclidean/Hilbertian Inner Product on vector space E: bilinear, symmetric, positive definite mapping x, y R. Simplest example: usual dot-product on R n : x = (x 1,..., x n ) t, y = (y 1,..., y n ) t, x y = x, y = n x i y i = x t Id y i=1 Id n n identity matrix. x T Ay, A symmetric, positive definite: inner product, x, y A. Slide 39/57 Aasa Feragen and François Lauze Differential Geometry September 22

92 Recall: Inner Products Euclidean/Hilbertian Inner Product on vector space E: bilinear, symmetric, positive definite mapping x, y R. Simplest example: usual dot-product on R n : x = (x 1,..., x n ) t, y = (y 1,..., y n ) t, x y = x, y = n x i y i = x t Id y i=1 Id n n identity matrix. x T Ay, A symmetric, positive definite: inner product, x, y A. Without subscript, will denote standard Euclidean dot-product (i.e. A = Id). Slide 39/57 Aasa Feragen and François Lauze Differential Geometry September 22

93 Orthogonality Norm Distance Orthogonality, vector norm, distance from inner products. x A y x, y A = 0, x 2 A = x, x A, d A (x, y) = x y A. Slide 40/57 Aasa Feragen and François Lauze Differential Geometry September 22

94 Orthogonality Norm Distance Orthogonality, vector norm, distance from inner products. x A y x, y A = 0, x 2 A = x, x A, d A (x, y) = x y A. There are norms and distances not from an inner product. Slide 40/57 Aasa Feragen and François Lauze Differential Geometry September 22

95 Inner Products and Duality Linear form h = (h 1,..., h n ) : R n R: h(x) = n i=1 h ix i. inner product, A on R n : h represented by a unique vector h A s.t h(x) = h A, x A h A is the dual of h (w.r.t, A ). Slide 41/57 Aasa Feragen and François Lauze Differential Geometry September 22

96 Inner Products and Duality Linear form h = (h 1,..., h n ) : R n R: h(x) = n i=1 h ix i. inner product, A on R n : h represented by a unique vector h A s.t h(x) = h A, x A h A is the dual of h (w.r.t, A ). for standard dot product: h = h 1. h n = h T! Slide 41/57 Aasa Feragen and François Lauze Differential Geometry September 22

97 Inner Products and Duality Linear form h = (h 1,..., h n ) : R n R: h(x) = n i=1 h ix i. inner product, A on R n : h represented by a unique vector h A s.t h(x) = h A, x A h A is the dual of h (w.r.t, A ). for standard dot product: h = h 1. h n = h T! for general inner product, A h A = A 1 h = A 1 h T. Slide 41/57 Aasa Feragen and François Lauze Differential Geometry September 22

98 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 42/57 Aasa Feragen and François Lauze Differential Geometry September 22

99 Riemannian Metric Riemannian metric on an m dimensional manifold = smooth family g P of inner products on the tangent spaces T P M of M u, v T P M g p(u, v) := u, v P R With it, one can compute length of vectors in tangent spaces, check orthogonality, etc... Slide 43/57 Aasa Feragen and François Lauze Differential Geometry September 22

100 Riemannian Metric Riemannian metric on an m dimensional manifold = smooth family g P of inner products on the tangent spaces T P M of M u, v T P M g p(u, v) := u, v P R With it, one can compute length of vectors in tangent spaces, check orthogonality, etc... Given a global parametrization ϕ: (x) = (x 1,..., x n ) ϕ(x) M, it corresponds to a smooth family of symmetric positive definite matrices: g x = g x11... g x1n.. g xn1... g xnn Slide 43/57 Aasa Feragen and François Lauze Differential Geometry September 22

101 Riemannian Metric Riemannian metric on an m dimensional manifold = smooth family g P of inner products on the tangent spaces T P M of M u, v T P M g p(u, v) := u, v P R With it, one can compute length of vectors in tangent spaces, check orthogonality, etc... Given a global parametrization ϕ: (x) = (x 1,..., x n ) ϕ(x) M, it corresponds to a smooth family of symmetric positive definite matrices: g x = g x11... g x1n.. g xn1... g xnn u = n i=1 u i xi, v = n i=1 v i xi u, v x = (u 1,..., u n )g x (v 1,..., v n ) t Slide 43/57 Aasa Feragen and François Lauze Differential Geometry September 22

102 Riemannian Metric Riemannian metric on an m dimensional manifold = smooth family g P of inner products on the tangent spaces T P M of M u, v T P M g p(u, v) := u, v P R With it, one can compute length of vectors in tangent spaces, check orthogonality, etc... Given a global parametrization ϕ: (x) = (x 1,..., x n ) ϕ(x) M, it corresponds to a smooth family of symmetric positive definite matrices: g x = g x11... g x1n.. g xn1... g xnn u = n i=1 u i xi, v = n i=1 v i xi u, v x = (u 1,..., u n )g x (v 1,..., v n ) t A smooth manifold with a Riemannian metric is a Riemannian manifold. Slide 43/57 Aasa Feragen and François Lauze Differential Geometry September 22

103 Riemannian Metric Slide 43/57 Aasa Feragen and François Lauze Differential Geometry September 22

104 Example: Induced Riemannian metric on submanifolds of R n Inner product from R n restricts to inner product on M R n Frobenius metric on P(n) P(n) is a convex subset of R (n2 +n)/2 The Euclidean inner product defines a Riemannian metric on P(n) Rightmost figure from Fletcher, Joshi, Principal Geodesic Analysis on Symmetric Spaces: Statistics of Diffusion Tensors, CVAMIA04 Slide 44/57 Aasa Feragen and François Lauze Differential Geometry September 22

105 Example: Fisher information metric Smooth manifold M = ϕ(u) represents a family of probability distributions (M is a statistical model), U R m Each point P = ϕ(x) M is a probability distribution P : Z R >0 The Fisher information metric of M at P x = ϕ(x) in coordinates ϕ defined by: log P x (z) log P x (z) g ij (x) = P x (z)dz x i x j Z Slide 45/57 Aasa Feragen and François Lauze Differential Geometry September 22

106 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 46/57 Aasa Feragen and François Lauze Differential Geometry September 22

107 Invariance of the Fisher information metric Obtaining a Riemannian metric g on the left chart by pulling g back from the right chart: g ij = g( (, ) = g d(ψ 1 ϕ)( ), d(ψ 1 ϕ)( ) ). x i x i x i x j Claim: g ij = m m k=1 l=1 g kl v k v l x i x j Slide 47/57 Aasa Feragen and François Lauze Differential Geometry September 22

108 Invariance of the Fisher information metric Claim: g ij = m m k=1 l=1 g kl v k v l x i x j Proof: ( = g g ij = g = m ) x i, ( x j m v k k=1 x i = m k=1 Slide 47/57 Aasa Feragen and François Lauze Differential Geometry September 22 v k, m l=1 m v k v l k=1 l=1 x i x j g m l=1 g kl v k x i ( v l x j v l x j ) v l ) v l v k,

109 Invariance of the Fisher information metric Fact: g ij = m m k=1 l=1 g kl v k v l x i x j Pulling the Fisher information metric g from right to left: g ij = m k=1 = m m l=1 g kl v k v l x i x j m log Pv (z) log P v (z) k=1 l=1 v k ) ( v l log P v (z) v m k v k x i l=1 log P x (z) x j P x (z)dz = ( m k=1 = log P x (z) x i P v (z)dz v k log P v (z) v l Result: Formula invariant of parametrization v l x i v l x j x j ) P v (z)dz Slide 47/57 Aasa Feragen and François Lauze Differential Geometry September 22

110 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 48/57 Aasa Feragen and François Lauze Differential Geometry September 22

111 Riemannian metrics and distances Path length in metric spaces: Let (X, d) be a metric space. The length of a curve c : [a, b] X is n 1 l(c) = sup a=t0 t 1... t n=b d(c(t i, t i+1 )). (3.1) i=0 Slide 49/57 Aasa Feragen and François Lauze Differential Geometry September 22

112 Riemannian metrics and distances Path length in metric spaces: Let (X, d) be a metric space. The length of a curve c : [a, b] X is n 1 l(c) = sup a=t0 t 1... t n=b d(c(t i, t i+1 )). (3.1) i=0 Approach supremum through segments c(t i, t i+1 ) of length 0 Slide 49/57 Aasa Feragen and François Lauze Differential Geometry September 22

113 Riemannian metrics and distances Path length in metric spaces: Let (X, d) be a metric space. The length of a curve c : [a, b] X is n 1 l(c) = sup a=t0 t 1... t n=b d(c(t i, t i+1 )). (3.1) i=0 Approach supremum through segments c(t i, t i+1 ) of length 0 Intuitive path length on Riemannian manifolds: Riemannian metric g on M defines norm in T P M Slide 49/57 Aasa Feragen and François Lauze Differential Geometry September 22

114 Riemannian metrics and distances Path length in metric spaces: Let (X, d) be a metric space. The length of a curve c : [a, b] X is n 1 l(c) = sup a=t0 t 1... t n=b d(c(t i, t i+1 )). (3.1) i=0 Approach supremum through segments c(t i, t i+1 ) of length 0 Intuitive path length on Riemannian manifolds: Riemannian metric g on M defines norm in T P M Locally a good approximation for use with (3.1) Slide 49/57 Aasa Feragen and François Lauze Differential Geometry September 22

115 Riemannian metrics and distances Path length in metric spaces: Let (X, d) be a metric space. The length of a curve c : [a, b] X is n 1 l(c) = sup a=t0 t 1... t n=b d(c(t i, t i+1 )). (3.1) i=0 Approach supremum through segments c(t i, t i+1 ) of length 0 Intuitive path length on Riemannian manifolds: Riemannian metric g on M defines norm in T P M Locally a good approximation for use with (3.1) This will be made precise in Francois lecture! Slide 49/57 Aasa Feragen and François Lauze Differential Geometry September 22

116 Geodesics as length-minimizing curves We have a concept of path length l(c) for paths c : [a, b] M Slide 50/57 Aasa Feragen and François Lauze Differential Geometry September 22

117 Geodesics as length-minimizing curves We have a concept of path length l(c) for paths c : [a, b] M A geodesic from P to Q in M is a path c : [a, b] X such that c(a) = P, c(b) = Q and l(c) = inf cp Q l(c P Q ). Slide 50/57 Aasa Feragen and François Lauze Differential Geometry September 22

118 Geodesics as length-minimizing curves We have a concept of path length l(c) for paths c : [a, b] M A geodesic from P to Q in M is a path c : [a, b] X such that c(a) = P, c(b) = Q and l(c) = inf cp Q l(c P Q ). The distance function d(p, Q) = inf cp Q l(c P Q ) is a distance metric on the Riemannian manifold (M, g). Slide 50/57 Aasa Feragen and François Lauze Differential Geometry September 22

119 Geodesics as length-minimizing curves We have a concept of path length l(c) for paths c : [a, b] M A geodesic from P to Q in M is a path c : [a, b] X such that c(a) = P, c(b) = Q and l(c) = inf cp Q l(c P Q ). The distance function d(p, Q) = inf cp Q l(c P Q ) is a distance metric on the Riemannian manifold (M, g). Can you see why? Slide 50/57 Aasa Feragen and François Lauze Differential Geometry September 22

120 Geodesics as length-minimizing curves We have a concept of path length l(c) for paths c : [a, b] M A geodesic from P to Q in M is a path c : [a, b] X such that c(a) = P, c(b) = Q and l(c) = inf cp Q l(c P Q ). The distance function d(p, Q) = inf cp Q l(c P Q ) is a distance metric on the Riemannian manifold (M, g). Can you see why? Do geodesics always exist? Slide 50/57 Aasa Feragen and François Lauze Differential Geometry September 22

121 Example: Riemannian geodesics between 1-dimensional Gaussian distributions Space parametrized by (µ, σ) R R + Metric 1: Euclidean inner product Euclidean geodesics Metric 2: Fisher information metric View in plane: 2 Middle figure from Costa et al, Fisher information distance: a geometrical reading Slide 51/57 Aasa Feragen and François Lauze Differential Geometry September 22

122 Outline 1 Motivation Nonlinearity Recall: Calculus in R n 2 Differential Geometry Smooth manifolds Building Manifolds Tangent Space Vector fields Differential of smooth map 3 Riemannian metrics Introduction to Riemannian metrics Recall: Inner Products Riemannian metrics Invariance of the Fisher information metric A first take on the geodesic distance metric A first take on curvature Slide 52/57 Aasa Feragen and François Lauze Differential Geometry September 22

123 A first take on curvature Curvature in metric spaces defined by comparison with model spaces of known curvature. Slide 53/57 Aasa Feragen and François Lauze Differential Geometry September 22

124 A first take on curvature Curvature in metric spaces defined by comparison with model spaces of known curvature. Positive curvature model spaces. Spheres of curvature κ > 0: Flat model space: Euclidean plane Negatively curved model spaces: Hyperbolic space of curvature κ > 0 Slide 53/57 Aasa Feragen and François Lauze Differential Geometry September 22

125 A first take on curvature Figure : Left: Geodesic triangle in a negatively curved space. Right: Comparison triangle in the plane. A CAT (κ) space is a metric space in which geodesic triangles are thinner than for their comparison triangles in the model space M κ ; that is, d(x, a) d( x, ā). A locally CAT (κ) space has curvature bounded from above by κ. Geodesic triangles are useful for intuition and proofs! Slide 53/57 Aasa Feragen and François Lauze Differential Geometry September 22

126 Example: The two metrics on 1-dimensional Gaussian distributions Metric 1: Euclidean inner product: FLAT Metric 2: Fisher information metric: HYPERBOLIC (OBS: Not hyperbolic for any family of distributions) View in plane: 2 Slide 54/57 Aasa Feragen and François Lauze Differential Geometry September 22

127 Example: The two metrics on 1-dimensional Gaussian distributions Metric 1: Euclidean inner product: FLAT Metric 2: Fisher information metric: HYPERBOLIC (OBS: Not hyperbolic for any family of distributions) View in plane: 2 Slide 54/57 Aasa Feragen and François Lauze Differential Geometry September 22

128 Example: The two metrics on 1-dimensional Gaussian distributions Metric 1: Euclidean inner product: FLAT Metric 2: Fisher information metric: HYPERBOLIC (OBS: Not hyperbolic for any family of distributions) View in plane: 2 You will see these again with Stefan! Slide 54/57 Aasa Feragen and François Lauze Differential Geometry September 22

129 Relation to sectional curvature CAT (κ) is a weak notion of curvature Stronger notion of sectional curvature (requires a little more Riemannian geometry) Theorem A smooth Riemannian manifold M is (locally) CAT (κ) if and only if the sectional curvature of M is κ. Slide 55/57 Aasa Feragen and François Lauze Differential Geometry September 22

130 Example of insight with CAT (κ): MDS and manifold learning lie to you Given a distance matrix D ij = d(x i, x j ) for a dataset X = {x 1,..., x n } residing on a manifold M, where d is a geodesic metric. Slide 56/57 Aasa Feragen and François Lauze Differential Geometry September 22

131 Example of insight with CAT (κ): MDS and manifold learning lie to you Given a distance matrix D ij = d(x i, x j ) for a dataset X = {x 1,..., x n } residing on a manifold M, where d is a geodesic metric. Assume that Z = {z 1,..., z n } R d is an embedding of X obtained through MDS or manifold learning. Slide 56/57 Aasa Feragen and François Lauze Differential Geometry September 22

132 Example of insight with CAT (κ): MDS and manifold learning lie to you Given a distance matrix D ij = d(x i, x j ) for a dataset X = {x 1,..., x n } residing on a manifold M, where d is a geodesic metric. Assume that Z = {z 1,..., z n } R d is an embedding of X obtained through MDS or manifold learning. Common belief: If d large, then Z is a good (perfect?) representation of X. Slide 56/57 Aasa Feragen and François Lauze Differential Geometry September 22

133 Example of insight with CAT (κ): MDS and manifold learning lie to you Truth: If there exists a map f : M R d such that f (a) f (b) = d(a, b) for all a, b M, then Slide 56/57 Aasa Feragen and François Lauze Differential Geometry September 22

134 Example of insight with CAT (κ): MDS and manifold learning lie to you Truth: If there exists a map f : M R d such that f (a) f (b) = d(a, b) for all a, b M, then f maps geodesics to straight lines M is CAT (0) M is not CAT (κ) for any κ < 0 Slide 56/57 Aasa Feragen and François Lauze Differential Geometry September 22

135 Example of insight with CAT (κ): MDS and manifold learning lie to you Truth: If there exists a map f : M R d such that f (a) f (b) = d(a, b) for all a, b M, then f maps geodesics to straight lines M is CAT (0) M is not CAT (κ) for any κ < 0 M is flat Slide 56/57 Aasa Feragen and François Lauze Differential Geometry September 22

136 Example of insight with CAT (κ): MDS and manifold learning lie to you Truth: If there exists a map f : M R d such that f (a) f (b) = d(a, b) for all a, b M, then f maps geodesics to straight lines M is CAT (0) M is not CAT (κ) for any κ < 0 M is flat That is, if M is not flat, MDS and manifold learning lie to you Slide 56/57 Aasa Feragen and François Lauze Differential Geometry September 22