SELEIGS

Select eigenvalues from a list

    J = SELEIGS(L,ALF)

Input
L	List of eigenvalues
ALF	Parameter determining the dimensionality and the eigenvalue-based mapping
(0,1)	fraction of the total (absolute value) preserved variance  Inf - no dimensionality reduction, keeping all dimensions (it's noisy)  'p' - projection into a Euclidean space based on positive eigenvalues only  'PARp' - projection into a Euclidean space based on the PAR fraction of  positive eigenvalues; e.g. ALF = '0.9p'  'n' - projection into a Euclidean space based on negative eigenvalues only  'PARn' - projection into a (negative) Euclidean space based on the PAR fraction  of negative eigenvalues; e.g. ALF = '0.7n'  'P1pP2n'- projection into a Euclidean space based on the P1 positive eigenvalues  and P2 negative eigenvalues; e.g. ALF = '0.7p0.1n', ALF = '7p2n'
1	.. N - number of dimensions in total
[P1	P2] - P1 dimensions or preserved fraction of variance in the positive subspace  and P2 dimensions or preserved fraction of variance in the negative  subspace; e.g. ALF = [5 10], ALF = [0.9 0.1]

Output
J	Index of selected eigenvalues

Description

This is a low-level routine for PSEM, KPSEM, KPCA and PEPCA. From a list of  eigenvalues it selects the ones according to ALF.

See also

mappings, datasets, psem, kpsem, kpca, pepca,