KLMS

Karhunen Loeve Mapping, followed by scaling

    [W,FRAC] = KLMS(A,N)
    [W,N] = KLMS(A,FRAC)

Input
A	Dataset
N	or FRAC Number of dimensions (>= 1) or fraction of variance (< 1) to retain; if > 0, perform PCA; otherwise MCA. Default: N = inf.

Output
W	Affine Karhunen-Loeve mapping
FRAC	or N Fraction of variance or number of dimensions retained.

Description

First a Karhunen Loeve Mapping is performed (i.e. PCA or MCA on the average  prior-weighted class covariance matrix). The result is scaled by the mean  class standard deviations. For N and FRAC, see KLM.

Default N: select all ('pre-whiten' the average covariance matrix, i.e.  orthogonalize and scale). The resulting mapping has a unit average  covariance matrix.

See also

mappings, datasets, klm, pca,