Advanced Machine Learning & Perception

Transcription

1 Advanced Machine Learning & Perception Instructor: Tony Jebara

2 Topic 1 Introduction, researchy course, latest papers Going beyond simple machine learning Perception, strange spaces, images, time, behavior Info, policies, texts, web page Syllabus, Overview, Review Gaussian Distributions Representation & Appearance Based Methods Least Squares, Correlation, Gaussians, Bases Principal Components Analysis and its Shortcomings

3 About me Tony Jebara, Associate Professor in Computer Science Started at Columbia in 2002 PhD from MIT in Machine Learning Thesis: Discriminative, Generative and Imitative Learning (2001) Research Areas: (Columbia Machine Learning Lab, CEPSR 6LE5) Machine Learning Some Computer Vision

4 Course Web Page Some material & announcements will be online But, many things will be handed out in class such as photocopies of papers for readings, etc. Check EWS link to see deadlines, homework, etc. Available online, see TA info, etc. Follow the policies, homework, deadlines, readings closely please!

5 Syllabus Week 1: Introduction, Review of Basic Concepts, Representation Issues, Vector and Appearance-Based Models, Correlation and Least Squared Error Methods, Bases, Eigenspace Recognition, Principal Components Analysis Week 2: onlinear Dimensionality Reduction, Manifolds, Kernel PCA, Locally Linear Embedding, Maximum Variance Unfolding, Minimum Volume Embedding Week 3: Maximum Entropy, Exponential Families, Maximum Entropy Discrimination, Large Margin Probability Models Week 4: Conditional Random Fields and Linear Models, Iterative Scaling and Majorization Week 5: Graphical Models, Multi-Class Support Vector Machines, Structured Support Vector Machines, Cutting Plane Algorithms Week 6: Kernels and Probabilistic Kernels

6 Syllabus Week 7: Feature Selection and Kernel Selection, Support Vector Machine Extensions Week 8: Meta-Learning and Multi-Task Support Vector Machines Week 9: Semi-Supervised Learning and Graph-Based Semi-Supervised Learning Week 10: High-Tree Width Graphical Models, Approximate Inference, Graph Structure Learning Week 11: Clustering, Spectral Clustering, ormalized Cuts. Week 12: Boosting, Mixtures of Experts, AdaBoost, Online Learning Week 13: Project Presentations Week 14: Project Presentations

7 Beyond Canned Learning Latest research methods, high dimensions, nonlinearities, dynamics, manifolds, invariance, unlabeled, feature selection Modeling Images / People / Activity / Time Series / MoCAP

8 Representation & Vectorization How to represent our data? Images, time series, genes Vectorization: the poor man s representation Almost anything can be written as a long vector E.g. image is read lexicographically RGB of eachpixel is added to a vector Or, a gene sequence can be written as a binary vector GATACAC = [ ] For images, this is called Appearance Based representation But, we lose many important properties this way We will fix this in later lectures For now, it is an easy way to proceed

9 Least Squares Detection How to find a face in an image given a template? Template Matching and Sum-Squares-Difference (SSD) aïve: try all positions/scales, find least squares distance i * = arg min i 1,T Correlation and ormalized Correlation Could normalize length of all vectors (fixes lighting) ˆµ = µ µ i * = arg min i 1,T i * = arg min i 1,T 1 2 µ x i ( ) T ( µ x ) i 1 2 µ x i ( ) 1 2 ˆµTˆµ 2ˆµ T ˆx i + ˆx it ˆx i 2 = arg max i 1,T ˆµ T ˆx i

10 Least Squares as Gaussian Model Minimum squared error is equivalent to maximum likelihood under a Gaussian i * 1 = arg min µ x 2 i 1,T 2 i 1 = arg max i 1,T log ( 2π) exp 1 µ x 2 D/2 2 i Can now treat it as a probabilistic problem Trying to find the most likely position (or sub-image) in search image given the Gaussian model of the template Define the log of the likelihood as: log p( x θ) For a Gaussian probability or likelihood is: p( x i θ) = 1 ( 2π) exp 1 µ x 2 D/2 2 i

11 Multivariate Gaussian Gaussian extend to D-dimensions and have 2 parameters: Mean vector µ vector (translates) Covariance matrix Σ (stretches and rotates) p( x µ,σ) 1 = exp 1 ( ( 2π) x µ ) T Σ 1 x µ D/2 Σ 2 x,µ R D, Σ R D D ( ) Max and expectation = µ Mean is any real vector, variance is now Σ matrix Covariance matrix is positive semi-definite Covariance matrix is symmetric eed matrix inverse (inv) eed matrix determinant (det) eed matrix trace operator (trace)

12 Max Likelihood Gaussian How to make face detector to work on all faces? Why use a template? How can we use many templates? Have IID samples of template vectors..: Represent IID samples with parameters as network: x 1,x 2,x 3,,x More efficiently drawn using Replicator Plate i = 1 Let us get a good Gaussian from these many templates. Standard approach: Max Likelihood log p x i ( µ,σ) 1 = log exp 1 x i µ 2π 2 ( ) D/2 Σ ( ) T Σ 1 ( x i µ )

13 Max Likelihood Gaussian Max over µ x T x x µ µ 1 log exp 1 ( 2π) x i µ D/2 Σ 2 ( ) T Σ 1 ( x i µ ) = 0 ( ) T Σ 1 ( x i µ ) D log 2π 1 log Σ 1 x i µ = = 2x T see Jordan Ch. 12, get sample mean For Σ need Trace operator: and several properties: tr A ( x i µ ) T Σ 1 = 0 ( ) = tr A T ( ) = tr ( BA) ( ) = tr ( A) tr AB tr BAB 1 µ = 1 ( ) D = A dd d =1 tr ( xx T A) = tr ( x T Ax) = x T Ax x i

14 Max Likelihood Gaussian Likelihood rewritten in trace notation: l = D 2 log 2π 1 2 log Σ 1 2 x i µ Max over A=Σ -1 use properties: Get sample covariance: = D 2 log 2π + 2 log Σ = D 2 log 2π + 2 log Σ = D 2 log 2π + 2 log A 1 2 l A = = 2 Σ 1 2 log A A = A 1 ( A 1 ) T 1 2 ( ) T Σ 1 ( x i µ ) tr ( x i µ ) T Σ 1 ( x i µ ) tr x i µ tr x i µ ( )( x i µ ) T Σ 1 ( )( x i µ ) T A ( ) T tr BA ( x i µ ) x i µ l A = BT ( x i µ ) x i µ ( ) T A = 0 Σ = 1 ( ) T T ( x i µ ) x i µ ( ) T

15 Principal Components Analysis Problem: for high dimensional data, D is large Storing Σ, inverting Σ -1 and determining Σ are expensive! Idea: limit Gaussian model to directions of high variance Use Principal Components Analysis to mimic Σ

16 Principal Components Analysis PCA approximates each datapoint as a mean vector plus steps along eigenvector directions. E.g. c i steps along v x x i 1 ( ) x ( i 2) µ 1 ( ) µ ( 2) v 1 +c i v 2 ( ) ( ) More generally, PCA uses a set of eigenvectors M (where M<<D) x i ˆx i = M µ + c j =1 ijv j PCA selects { µ,c ij, v } 2 j to minimize x i ˆx i The optimal directions are eigenvectors of covariance Which directions to keep: highest eigenvalues (variances)

17 Principal Components Analysis Use eigenvectors, mean & coefficients to approximate data x i M µ + c j =1 ijv j PCA finds eigenvectors by decomposing covariance matrix: Σ =V ΛV T = λ i vi vi T Σ 11 Σ 12 Σ 13 Σ 12 Σ 22 Σ 23 Σ 13 Σ 23 Σ 33 D = v 1 v2 v3 Eigenvectors are orthonormal: Eigenvalues are non-negative and non-increasing PCA selects the M eigenvectors with largest eigenvalues M Truncating gives an approximate covariance: PCA finds coefficients by: c ij = x i µ ˆΣ = λ 1 λ 2 λ D 0 λ λ λ 3 T v i v j = δ ij ( ) T v j v1 v2 v3 T λ i vi vi T

18 PCA via the Snapshot Method Careful how big is the covariance matrix? Assume 1000 images each containing D=20,000 pixels It is DxD pixels, that s unstoreable! Also, finding the eigenvectors or inverting DxD, requires O(D 3 )! First compute mean of all data (easy) and subtract it from each point Instead of: Σ = 1 T x i x i compute Φ where Φ = x T i,j i x j Then find eigendecomposition of Gram matrix Φ= V Λ V T Eigenvectors of Σ are then: j =1 ( ) v i x j v i j

19 PCA via the Snapshot Method { x 1,,x } = µ { µ + c 1j v j, } =

20 Truncated Gaussian Detection Approximate Σ with PCA plus spherical term via Specific Eigenvalues & Eigenvectors Spherical Eigenvalues p( x µ, ˆΣ ) Σ = D λ k v k v T k k=1 ˆΣ = M λ k v k v T k + k=1 ˆΣ 1 M 1 = ˆΣ D k=m +1 v λ k k v T k + D 1 k=1 k=m +1 ˆΣ 1 M 1 = k=1( 1 ρ)v k v T k + ˆΣ = 1 I ρ M λ k λ k=1 k D k=m +1 ρ ρv k v k T T ρ v k v k ρ = 1 D M = 1 D M = 1 D M Σ kk = 1 D λ k=m +1 k ( D λ k=1 k M λ ) k=1 k ( D Σ k=1 kk M λ ) k=1 k ( ( k) µ ( k) ) 2 x i

21 Truncated Gaussian Detection Instead of minimizing squared error, use Gaussian model p x µ, ˆΣ ( ) 1 = exp 1 ( ( 2π) x µ ) T ˆΣ 1 ( x µ ) D/2 ˆΣ 2 Use Snapshot PCA to efficiently store the big covariance This maximum likelihood Gaussian model achieved state of the art face finding as evaluated by IST/DARPA (Moghaddam et al., 2000) Top performer after 2000 is Viola-Jones (boosted cascade)

22 Gaussian Face Recognition Instead of modeling face images with Gaussian model the difference of two face images with a Gaussian Each difference of all pairs of images in our data is represented as a D-dimensional vector x Also have a binary label y, y=1 same person same, y=0 not x R D y 0,1 { } y i = 1,x i = - y j = 0,x j = - One Gaussian for same-face deltas another for different people deltas

23 Gaussian Classification Have two classes, each with their own Gaussian: i = 1 p x µ,σ,y Generation: 1) flip a coin, get y 2) pick Gaussian y, sample x from it Maximum Likelihood: l = {( x 1,y 1 ),,( x,y )} x R D y 0,1 ( ) ( ) + ( ) + log p x i,y i α,µ,σ = log p y i α p( α)p ( µ )p( Σ)p ( y α)p( x y,µ,σ) p( y α) = α y ( 1 α) 1 y ( ) = ( x µ y,σ ) y ( ) ( ) + log p( x i µ 1,Σ 1 ) log p x i y i,µ,σ = log p y i α log p x i µ 0,Σ 0 yi 0 { } yi 1

24 Gaussian Classification Max Likelihood can be done separately for the 3 terms l = ( ) + log p y i α log p x i µ 0,Σ 0 yi 0 ( ) + log p( x i µ 1,Σ 1 ) yi 1 Count # of pos & neg examples (class prior): α = Get mean & cov of negatives and mean & cov of positives: µ 0 = 1 0 x y i 0 i Σ 0 = 1 ( x 0 i µ )( 0 x i µ ) T yi 0 0 µ 1 = 1 1 x y i 1 i Σ 1 = 1 ( x 1 i µ )( 1 x i µ ) T yi 1 Given (x,y) pair, can now compute likelihood p( 1 x,y) To make classification, a bit of Decision Theory Without x, can compute prior guess for y p( y) Give me x, want y, I need posterior p( y x) Bayes Optimal Decision: ŷ = arg max y= { 0,1 } p ( y x ) Optimal iff we have true probability

25 Gaussian Classification Example cases, plotting decision boundary when = 0.5 ( ) = p y = 1 x = p( x,y = 1) ( ) + p( x,y = 1) α ( x µ 1,Σ 1 ) ( ) + α ( x µ 1,Σ 1 ) p x,y = 0 ( 1 α) x µ 0,Σ 0 If covariances are equal: linear decision If covariances are different: quadratic decision

26 Intra-Extra Personal Gaussians Intrapersonal Gaussian model ( x µ 1,Σ ) 1 Covariance is approximated by these eigenvectors: Extrapersonal Gaussian model ( x µ 0,Σ ) 0 Covariances is approximated by these eigenvectors: Question: what are the Gaussian means? Probability a pair is the same person: ( ) = p y = 1 x α ( x µ 1,Σ 1 ) ( ) + α ( x µ 1,Σ 1 ) ( 1 α) x µ 0,Σ 0

27 Other Standard Bases There are other choices for the eigenvectors, not just PCA Could pick eigenvectors without looking at the data Just for their interesting properties x i µ + C c ij v j j =1 Fourier basis: denoises, only keeps smooth parts of image Wavelet basis: localized or windowed Fourier PCA: optimal least squares linear dataset reconstruction

28 Basis for atural Images What happens if we do PCA on all natural images instead of just faces? Get difference of Gaussian bases Like Gabor or Wavelet basis ot specific like faces Multi-scale & orientation Also called steerable filters Similar to visual cortex

29 Problems with Linear Bases Coefficient representation changes wildly if image rotates and so does p(x) The eigenspace is sensitive to rotations, translations and transformations of the image Simple linear/gaussian/pca models are not enough What worked for aligned faces breaks for general image datasets Most of the PCA eigenvectors and spectrum energy is wasted due to OLIEAR EFFECTS c ij = ( x i µ ) T v j