function [U,S,V] = svdecon(X) Input: X : m x n matrix Output: X = U*S*V' Description: Does equivalent to svd(X,'econ') but faster *** function [U,S,V] = svdsecon(X,k) Input: X : m x n matrix k : extracts the first k singular values Output: X = U*S*V' approximately (up to k) Description: Does equivalent to svds(X,k) but faster Requires that k < min(m,n) where [m,n] = size(X) This function is useful if k is much smaller than m and n or if X is sparse (see doc eigs) *** function [U,T,mu] = pcaecon(X,k) Input: X : m x n matrix Each column of X is a feature vector Output: X = U*T approximately (up to k) Description: Principal Component Analysis (PCA) Requires that k <= min(m,n) where [m,n] = size(X) *** function [U,T,mu] = pcasecon(X,k) Input: X : m x n matrix Each column of X is a feature vector Output: X = U*T approximately (up to k) Description: Principal Component Analysis (PCA) Requires that k <= min(m,n) where [m,n] = size(X) This function is useful if k is much smaller than m and n or if X is sparse (see doc eigs)