KLM KarhunenLoeve Mapping (PCA or MCA of mean covariance matrix)
[W,FRAC] = KLM(A,N)
DescriptionThe KarhunenLoeve Mapping performs a principal component analysis (PCA) or minor component analysis (MCA) on the mean class covariance matrix (weighted by the class prior probabilities). It finds a rotation of the dataset A to an Ndimensional linear subspace such that at least (for PCA) or at most (for MCA) a fraction FRAC of the total variance is preserved. PCA is applied when N (or FRAC) >= 0; MCA when N (or FRAC) < 0. If N is given (abs(N) >= 1), FRAC is optimised. If FRAC is given (abs(FRAC) < 1), N is optimised. Objects in a new dataset B can be mapped by B*W, W*B or by A*KLM([],N)*B. Default (N = inf): the features are decorrelated and ordered, but no feature reduction is performed. ALTERNATIVE V = KLM(A,0) Returns the cummulative fraction of the explained variance. V(N) is the cumulative fraction of the explained variance by using N eigenvectors. Use PCA for a principal component analysis on the total data covariance. Use FISHERM for optimizing the linear class separability (LDA). This function is basically a wrapper around pcaklm.m. See alsomappings, datasets, pcaklm, pcldc, klldc, pcam, fisherm,
