Neural Computation 0368-4149-01

1. Neural Computation0368-4149-01 Prof. Nathan Intrator Tuesday 16:00-19:00 Schreiber 007 Office hours: Wed 4-5 nin@tau.ac.il

2. Outline • Goals for neural learning - Unsupervised • Goals for statistical/computational learning • PCA • ICA • Exploratory Projection Pursuit • Search for non-Gaussian distributions • Practical implementations

3. Statistical Approach to Unsupervised Learning • Understanding the nature of data variability • Modeling the data (sometimes very flexible model) • Understanding the nature of the noise • Applying prior knowledge • Extracting features based on: • Prior knowledge • Class prediction • Unsupervised learning

4. Principal Component Analysis. Włodzisław Duch SCE, NTU, Singapore http://www.ntu.edu.sg/home/aswduch

5. Linear transformations – example 2D vectors X in a unit circle with mean (1,1); Y = A*X, A = 2x2 matrix The shape is elongated, rotated and the mean is shifted.

6. Invariant distances Euclidean distance is not invariant to general linear transformations This is invariant only for orthonormal matrices ATA = I that make rigid rotations, without stretching or shrinking distances. Idea: standardize the data in some way to create invariant distances.

7. Data standardization For each vector component X(j)T=(X1(j), ... Xd(j)),j=1 .. n calculate mean and std: n– number of vectors, d – their dimension Vector of mean feature values. Averages over rows.

8. Standard deviation Calculate standard deviation: Vector of mean feature values. Variance = square of standard deviation (std), sum of all deviations from the mean value. Transform X => Z, standardized data vectors

9. Std data Std data: zero mean and unit variance. Standardize data after making data transformation. Effect: data is invariant to scaling only (diagonal transformation). Distances are invariant, data distribution is the same. How to make data invariant to any linear transformations?

10. Data standardization example Transformation Vector of mean feature values. Variance check it! For our example Y=AX, assuming X means=1 and variances = 1 How to make this invariant?

11. Covariance matrix Variance (spread around mean value) + correlation between features. CX is d x d where X is d x n dimensional matrix of vectors shifted to their means. Covariance matrix is symmetric Cij = Cjiand positive definite. Diagonal elements are variances (square of std), si2 = Cii Spherical distribution of data has Cij=I (unit matrix). Elongated ellipsoids: large off-diagonal elements, strong correlations between features.

12. Mahalanobis distance Linear combinations of features leads to rotations and scaling of data. Mahalanobis distance: is invariant to linear transformations:

13. Principal components How to avoid correlated features? Correlations  covariance matrix is non-diagonal ! Solution: diagonalize it, then use transformation that makes it diagonal to de-correlate features. In matrix form, X, Y are dxn, Z, CX,CY are dxd C – symmetric, positive definite matrix XTCX > 0 for ||X||>0; its eigenvectors are orthonormal: its eigenvalues are all non-negative Z – matrix of orthonormal eigenvectors (because Z is real+symmetric), transforms X into Y, with diagonal CY, i.e. decorrelated.

14. Matrix form Eigen problem for C matrix in matrix form:

15. Principal components Y – principal components, or vectors X transformed using eigenvectors of CX Covariance matrix of transformed vectors is diagonal => ellipsoidal distribution of data. PCA: old idea, C. Pearson (1901), H. Hotelling 1933 Result: PC are linear combinations of all features, providing new uncorrelated features, with diagonal covariance matrix = eigenvalues. Small li small variance  data change little in direction Yi PCA minimizes C matrix reconstruction errors: Zi vectors for large liare sufficient to get: because vectors for small eigenvalues will have very small contribution to the covariance matrix.

16. Two components for visualization Diagonalization methods: see Numerical Recipes, www.nr.com New coordinate system: axis ordered according to variance = size of the eigenvalue. First k dimensions account for fraction of all variance (please note that li are variances); frequently 80-90% is sufficient for rough description.

17. PCA properties PC Analysis (PCA) may be achieved by: • transformation making covariance matrix diagonal • projecting the data on a line for which the sums of squares of distances from original points to projections is minimal. • orthogonal transformation to new variables that have stationary variances True covariance matrices are usually not known, estimated from data. This works well on single-cluster data; more complex structure may require local PCA, separately for each cluster. PC is useful for: finding new, more informative, uncorrelated features; reducing dimensionality: reject low variance features, reconstructing covariance matrices from low-dim data.

18. PCA Wisconsin example Wisconsin Breast Cancer data: • Collected at the University of Wisconsin Hospitals, USA. • 699 cases, 458 (65.5%) benign (red), 241 malignant (green). • 9 features: quantized 1, 2 .. 10, cell properties, ex: Clump Thickness, Uniformity of Cell Size, Shape, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli, Mitoses. 2D scatterograms do not show any structure no matter which subspaces are taken!

19. Example cont. PC gives useful information already in 2D. Taking first PCA component of the standardized data: If (Y1>0.41) then benign else malignant 18 errors/699 cases = 97.4% Transformed vectors are not standardized, std’s are below. Eigenvalues converge slowly, but classes are separated well.

20. PCA disadvantages Useful for dimensionality reduction but: • Largest variance determines which components are used, but does not guarantee interesting viewpoint for clustering data. • The meaning of features is lost when linear combinations are formed. Analysis of coefficients in Z1 and other important eigenvectors may show which original features are given much weight. PCA may be also done in an efficient way by performing singular value decomposition of the standardized data matrix. PCA is also called Karhuen-Loève transformation. Many variants of PCA are described in A. Webb, Statistical pattern recognition, J. Wiley 2002.

21. 2 skewed distributions PCA transformation for 2D data: First component will be chosen along the largest variance line, both clusters will strongly overlap, no interesting structure will be visible. In fact projection to orthogonal axis to the first PCA component has much more discriminating power. Discriminant coordinates should be used to reveal class structure.

22. High Dimensional Data Dimension Reduction Visualisation Classification Analysis Feature Extraction

23. Projection Pursuit what: An automated procedure that seeks interesting low dimensional projections of a high dimensional cloud by numerically maximizing an objective function or projection index. Huber, 1985

24. Projection Pursuit why: Curse of dimensionality • Less Robustness • worse mean squared error • greater computational cost • slower convergence to limiting distributions • … • Required number of labelled samples increases with dimensionality.

25. What is an interesting projection In general: the projection that reveals more information about the structure. In pattern recognition: a projection that maximises class separability in a low dimensional subspace.

26. Projection Pursuit Dimensional Reduction Find lower-dimensional projections of a high-dimensional point cloud to facilitate classification. Exploratory Projection Pursuit Reduce the dimension of the problem to facilitate visualization.

27. Projection Pursuit How many dimensions to use • for visualization • for classification/analysis Which Projection Index to use • measure of variation (Principal Components) • departure from normality (negative entropy) • class separability(distance, Bhattacharyya, Mahalanobis, ...) • …

28. Projection Pursuit Which optimization method to choose We are trying to find the global optimum among local ones • hill climbing methods (simulated annealing) • regular optimization routines with random starting points.

29. Timetable for Dimensionality reduction • Begin 16 April 1998 • Report on the state-of-the-art. 1 June 1998 • Begin software implementation 15 June 1998 • Prototype software presentation 1 November 1998