Machine Learning - MT & 14. PCA and MDS

Transcription

1 Machine Learning - MT & 14. PCA and MDS Varun Kanade University of Oxford November 21 & 23, 2016

2 Announcements Sheet 4 due this Friday by noon Practical 3 this week (continue next week if necessary) Revision Class for M.Sc. + D.Phil. Thu Week 9 (2pm & 3pm) Work through ML HT2016 Exam (Problem 3 is optional) 1

3 Supervised Learning: Summary Training data is of the form (x i, y i) where x i are features and y i is target We formulate a model: generative or discriminative Choose a suitable training criterion (loss function, maximum likelihood) Use optimisation procedure to learn parameters Use regularization or other techniques to reduce overfitting Use trained classifier to predict targets/labels on unseen x new 2

4 Unsupervised Learning Training data is of the form x 1,..., x N Infer properties about the data Search: Identify patterns in data Density Estimation: Learn the underlying distribution generating data Clustering: Group similar points together Today: Dimensionality Reduction 3

5 Outline Today, we ll study a technique for dimensionality reduction Principal Component Analysis (PCA) identifies a small number of directions which explain most variation in the data PCA can be kernelised Dimensionality reduction is important both for visualising and as a preprocessing step before applying other (typically unsupervised) learning algorithms 4

6 Principal Component Analysis (PCA) 5

7 Principal Component Analysis (PCA) 5

8 Principal Component Analysis (PCA) 5

9 Principal Component Analysis (PCA) 5

10 Principal Component Analysis (PCA) 5

11 PCA: Maximum Variance View PCA is a linear dimensionality reduction technique Find the directions of maximum variance in the data (x i) N i=1 Assume that data is centered, i.e., i xi = 0 Find a set of orthogonal vectors v 1,..., v k The first principal component (PC) v 1 is the direction of largest variance The second PC v 2 is the direction of largest variance orthogonal to v 1 The i th PC v i is the direction of largest variance orthogonal to v 1,..., v i 1 V D k gives projection z i = V T x i Z = XV for datapoint x i for entire dataset 6

12 PCA: Maximum Variance View We are given i.i.d. data (x i) N i=1; data matrix X Want to find v 1 R D, v 1 = 1, that maximizes Xv 1 2 Let z = Xv 1, so z i = x i v 1. We wish to find v 1 so that N i=1 z2 i is maximised. N zi 2 = z T z i=1 = v T 1 X T Xv 1 The maximum value attained by v T 1 X T Xv 1 for v 1 = 1 is the largest eigenvalue of X T X. The argmax is the corresponding eigenvector v 1. Find v 2, v 3,..., v k that are all successively orthogonal to previous directions and maximise (as yet unexplained variance) 7

13 PCA: Best Reconstruction We have i.i.d. data (x i) N i=1; data matrix X Find a k-dimensional linear projection that best represents the data Suppose V k R D k is such that columns of V k are orthogonal Project data X on to subspace defined by V Z = XV k Minimize reconstruction error N x i V k Vkx T i 2 i=1 8

14 Principal Component Analysis (PCA) 9

15 Equivalence between the Two Objectives: One PC Case Let v 1 be the direction of projection The point x is mapped to x = (v 1 x)v 1, where v 1 = 1 Maximum Variance Find v 1 that maximises N i=1 (v1 xi)2 Best Reconstruction Find v 1 that minimises: N N x i x i 2 = ( x i 2 2(x i x i) + x i 2) i=1 = i=1 N ( x i 2 2(v 1 x i) 2 + (v 1 x i) 2 v 1 2) i=1 N N = x i 2 (v 1 x i) 2 i=1 i=1 So the same v 1 satisfies the two objectives 10

16 Finding Principal Components: SVD Let X be the N D data matrix Pair of singular vectors u R N, v R D and singular value σ R + if σu = Xv and σv = X T u v is an eigenvector of X T X with eigenvalue σ 2 u is an eigenvector of XX T with eigenvalue σ 2 11

17 Finding Principal Components: SVD X = UΣV T (say N > D) Thin SVD: U is N D, Σ is D D, V is D D, U T U = V T V = I D Σ is diagonal with σ 1 σ 2 σ D 0 The first k principal components are first k columns of V Full SVD: U is N N, Σ = N D, V is D D. V and U are orthonormal matrices 12

18 Algorithm for finding PCs (when N > D) Constructing the matrix X T X takes time O(D 2 N) Eigenvectors of X T X can be computed in time O(D 3 ) Iterative methods to get top k singular (right) vectors directly: Initiate v 0 to be random unit norm vector Iterative Update: v t+1 = X T Xv t v t+1 = v t+1 / v t+1 until (approximate) convergence Update step only takes O(ND) time (compute Xv t first, then X T (Xv t )) This gives the singular vector corresponding to the largest singular value Subsequent singular vectors obtained by choosing v 0 orthogonal to previously identified singular vectors (this needs to be done at each iteration to avoid numerical errors creeping in) 13

19 Algorithm for finding PCs (when D N) Constructing the matrix XX T takes time O(N 2 D) Eigenvectors of XX T can be computed in time O(N 3 ) The eigenvectors give the left singular vectors, u i of X To obtain v i, we use the fact that v i = σ 1 X T u i Iterative method can be used directly as in the case when N > D 14

20 PCA: Reconstruction Error We have thin SVD: X = UΣV T Let V k be the matrix containing first k columns of V Projection on to k PCs: Z = XV k = U k Σ k, where U k is the matrix of the first k columns of U and Σ k is the k k diagonal submatrix for Σ of the top k singular values Reconstruction: X = ZVk T = U k Σ k Vk T N D Reconstruction error = x i V k Vkx T i 2 = i=1 j=k+1 This follows from the following calculations: σ 2 j D X = UΣV T = σ ju jvj T j=1 X X D F = j=k+1 σ 2 j k X = U k Σ k Vk T = σ ju jvj T j=1 15

21 Reconstruction of an Image using PCA 16

22 How many principal components to pick? Look for an elbow in the curve of reconstruction error vs # PCs 17

23 Application: Eigenfaces A popular application of PCA for face detection and recognition is known as Eigenfaces Face detection: Identify faces in a given image Face Recognition: Classification (or search) problem to identify a certain person 18

24 Application: Eigenfaces PCA on a dataset of face images. Each principal component can be thought of as being an element of a face. Source: 19

25 Application: Eigenfaces Detection: Each patch of the image can be checked to identify whether there is a face in it Recognition: Map all faces in terms of their principal components. Then use some distance measure on the projections to find faces that are most like the input image. Why use PCA for face detection? Even though images can be large, we can use the D N approach to be efficient The final model (the PCs) can be quite compact, can fit on cameras, phones Works very well given the simplicity of the model 20

26 Application: Latent Semantic Analysis X is an N D matrix, D is the size of dictionary x i is a vector of word counts (bag of words) Reconstruction using k eigenvectors X ZV T k, where Z = XV k z i, z j is probably a better notion of similarity than x i, x j V T k X Z Non-negative matrix factorisation has more natural interpretation, but is harder to compute 21

27 PCA: Beyond Linearity 22

28 PCA: Beyond Linearity 22

29 PCA: Beyond Linearity 22

30 PCA: Beyond Linearity 22

31 Projection: Linear PCA 23

32 Projection: Kernel PCA 24

33 Kernel PCA Suppose our original data is, for example, x R 2 We could perform degree 2 polynomial basis expansion as: φ(x) = [1, 2x 1, 2x 2, x 2 1, x 2 2, ] T 2x 1x 2 Recall that we can compute the inner products φ(x) φ(x ) efficiently using the kernel trick φ(x) φ(x ) = 1 + 2x 1x 1 + 2x 2x 2 + x 2 1(x 1) 2 + x 2 2(x 2) 2 + 2x 1x 2x 1x 2 = (1 + x 1x 2 + x 1x 2) 2 = (1 + x x ) 2 =: κ(x, x ) 25

34 Kernel PCA Suppose we use the feature map: φ : R D R M Let φ(x) be the N M matrix We want find the singular vectors of φ(x) (eigenvectors of φ(x) T φ(x)) However, in general M N (in fact M could be infinite for some kernels) Instead we ll find the eigenvectors of φ(x)φ(x) T, the kernel matrix 26

35 Kernel PCA Recall that the kernel matrix is: κ(x 1, x 1) κ(x 1, x 2) κ(x 1, x N ) κ(x 2, x 1) κ(x 2, x 2) κ(x 2, x N ) K = φ(x)φ(x) T = κ(x N, x 1) κ(x N, x 2) κ(x N, x N ) Let u R N be an eigenvector of K, (left singular vector of φ(x)) The corresponding principal component v R M is σ 1 φ(x) T u We won t express v explicitly, instead we can compute projections of a new datapoint x new on to the principal component v using the kernel function: φ(x new) T v = σ 1 φ(x new) T φ(x) T u = σ 1 [κ(x new, x 1 ), κ(x new, x 2 ),, κ(x new, x N )]u So in order to compute projections onto principal components we do not need to store the principal components explicitly! 27

36 Kernel PCA For PCA, we assumed that the datamatrix X is centered, i.e., i xi = 0 However, this is not the case for the matrix φ(x) Instead we can consider: φ(x i) = φ(x i) 1 N N φ(x k ) k=1 The corresponding matrix K is given by the entries K ij = κ(x i, x j) 1 N N κ(x i, x l ) 1 N l=1 N l=1 κ(x j, x l ) + 1 N 2 N k=1 l=1 N κ(x l, x k ) Succintly, if O is the matrix of all with every entry 1/N, i.e., O = 11 T /N K = K OK KO + OKO To perform kernel PCA, we need to find the eigenvectors of K 28

37 Projection: PCA vs Kernel PCA 29

38 Kernel PCA Applications Kernel PCA is not necessarily very useful for visualisation Also, kernel PCA does not directly give a useful way to construct a low-dimensional reconstruction of the original data Most powerful uses of kernel PCA are in other machine learning applications After kernel PCA preprocessing, we may get higher accuracy for classification, clustering, etc. 30

39 PCA Summary Algorithm: We ve expressed PCA as SVD of data matrix X Equivalently, we can use eigendecomposition of the matrix X T X Running Time: O(NDk) to compute k principal components (avoid computing the matrix X T X) PCs are uncorrelated, but there may be non-linear (higher-order) effects PCA depends on scale or units of measurement; it may be a good idea to standardize data PCA is sensitive to outliers PCA can be kernelised: Useful as preprocessing for further ML applications, rather than visualisation 31