Presentation Transcript

1. Principal Component Analysis Jieping Ye Department of Computer Science and Engineering Arizona State University https://www.public.asu.edu/~jye02

2. Outline of lecture • What is feature reduction? • Why feature reduction? • Feature reduction algorithms • Principal Component Analysis (PCA) • Nonlinear PCA using Kernels

3. What is feature reduction? • Feature reduction refers to the mapping of the original high-dimensional data onto a lower-dimensional space. • Criterion for feature reduction can be different based on different problem settings. • Unsupervised setting: minimize the information loss • Supervised setting: maximize the class discrimination • Given a set of data points of p variables Compute the linear transformation (projection)

4. What is feature reduction? Original data reduced data Linear transformation

5. High-dimensional data Gene expression Face images Handwritten digits

6. Outline of lecture • What is feature reduction? • Why feature reduction? • Feature reduction algorithms • Principal Component Analysis • Nonlinear PCA using Kernels

7. Why feature reduction? • Most machine learning and data mining techniques may not be effective for high-dimensional data • Curse of Dimensionality • Query accuracy and efficiency degrade rapidly as the dimension increases. • The intrinsic dimension may be small. • For example, the number of genes responsible for a certain type of disease may be small.

8. Why feature reduction? • Visualization: projection of high-dimensional data onto 2D or 3D. • Data compression: efficient storage and retrieval. • Noise removal: positive effect on query accuracy.

9. Application of feature reduction • Face recognition • Handwritten digit recognition • Text mining • Image retrieval • Microarray data analysis • Protein classification

10. Outline of lecture • What is feature reduction? • Why feature reduction? • Feature reduction algorithms • Principal Component Analysis • Nonlinear PCA using Kernels

11. Feature reduction algorithms • Unsupervised • Latent Semantic Indexing (LSI): truncated SVD • Independent Component Analysis (ICA) • Principal Component Analysis (PCA) • Canonical Correlation Analysis (CCA) • Supervised • Linear Discriminant Analysis (LDA) • Semi-supervised • Research topic

12. Outline of lecture • What is feature reduction? • Why feature reduction? • Feature reduction algorithms • Principal Component Analysis • Nonlinear PCA using Kernels

13. What is Principal Component Analysis? • Principal component analysis (PCA) • Reduce the dimensionality of a data set by finding a new set of variables, smaller than the original set of variables • Retains most of the sample's information. • Useful for the compression and classification of data. • By information we mean the variation present in the sample, given by the correlations between the original variables. • The new variables, called principal components (PCs), are uncorrelated, and are ordered by the fraction of the total information each retains.

14. Geometric picture of principal components (PCs) • the 1st PC is a minimum distance fit to a line in X space • the 2nd PC is a minimum distance fit to a line in the plane perpendicular to the 1st PC PCs are a series of linear least squares fits to a sample, each orthogonal to all the previous.

15. Algebraic definition of PCs Given a sample of n observations on a vector of p variables define the first principal component of the sample by the linear transformation where the vector is chosen such that is maximum.

16. Algebraic derivation of PCs To find first note that where is the covariance matrix. In the following, we assume the Data is centered.

17. Algebraic derivation of PCs Assume Form the matrix: then Obtain eigenvectors of S by computing the SVD of X:

18. Algebraic derivation of PCs To find that maximizes subject to Let λ be a Lagrange multiplier is an eigenvector of S therefore corresponding to the largest eigenvalue

19. Algebraic derivation of PCs To find the next coefficient vector maximizing uncorrelated subject to and to First note that then let λ and φ be Lagrange multipliers, and maximize

20. Algebraic derivation of PCs

21. Algebraic derivation of PCs We find that is also an eigenvector of S whose eigenvalue is the second largest. In general • The kth largest eigenvalue of S is the variance of the kth PC. • The kth PC retains the kth greatest fraction of the variation • in the sample.

22. Algebraic derivation of PCs • Main steps for computing PCs • Form the covariance matrix S. • Compute its eigenvectors: • Use the first d eigenvectors to form the d PCs. • The transformation G is given by

23. Optimality property of PCA Reconstruction Dimension reduction Original data

24. Optimality property of PCA Main theoretical result: The matrix G consisting of the first d eigenvectors of the covariance matrix S solves the following min problem: reconstruction error PCA projection minimizes the reconstruction error among all linear projections of size d.

25. Applications of PCA • Eigenfaces for recognition. Turk and Pentland. 1991. • Principal Component Analysis for clustering gene expression data. Yeung and Ruzzo. 2001. • Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum. Lilien. 2003.

26. PCA for image compression d=1 d=2 d=4 d=8 Original Image d=16 d=32 d=64 d=100

27. Outline of lecture • What is feature reduction? • Why feature reduction? • Feature reduction algorithms • Principal Component Analysis • Nonlinear PCA using Kernels

28. Motivation Linear projections will not detect the pattern.

29. Nonlinear PCA using Kernels • Traditional PCA applies linear transformation • May not be effective for nonlinear data • Solution: apply nonlinear transformation to potentially very high-dimensional space. • Computational efficiency: apply the kernel trick. • Require PCA can be rewritten in terms of dot product. More on kernels later

30. Nonlinear PCA using Kernels Rewrite PCA in terms of dot product The covariance matrix S can be written as Let v be The eigenvector of S corresponding to nonzero eigenvalue Eigenvectors of S lie in the space spanned by all data points.

31. Nonlinear PCA using Kernels The covariance matrix can be written in matrix form: Any benefits?

32. Nonlinear PCA using Kernels Next consider the feature space: The (i,j)-th entry of is Apply the kernel trick: K is called the kernel matrix.

33. Nonlinear PCA using Kernels • Projection of a test point x onto v: Explicit mapping is not required here.

34. Reference • Principal Component Analysis. I.T. Jolliffe. • Kernel Principal Component Analysis. Schölkopf, et al. • Geometric Methods for Feature Extraction and Dimensional Reduction. Burges.