PCLDC

Linear classifier using PC expansion on the joint data.

    W = PCLDC(A,N)
    W = PCLDC(A,ALF)

Input
A	Dataset
N	Number of eigenvectors
ALF	Total explained variance (default: ALF = 0.9)

Output
W	Mapping

Description

Finds the linear discriminant function W for the dataset A computing the LDC on a projection of the data on the first N eigenvectors of the total dataset (Principle Component Analysis).

When ALF is supplied the number of eigenvalues is chosen such that at  least a part ALF of the total variance is explained.

If N (ALF) is NaN it is optimised by REGOPTC.

See also

mappings, datasets, klldc, klm, fisherm, regoptc,