KLLDC

Linear classifier built on the KL expansion of the common covariance matrix

   W = KLLDC(A,N)
   W = KLLDC(A,ALF)

Input
A	Dataset
N	Number of significant eigenvectors
ALF	0 < ALF <= 1, percentage of the total variance explained (default: 0.9)

Output
W	Linear classifier

Description

Finds the linear discriminant function W for the dataset A. This is done  by computing the LDC on the data projected on the first eigenvectors of

the	averaged covariance matrix of the classes. Either first N eigenvectors
are	used or the number of eigenvectors is determined such that ALF, the
percentage	of the total variance is explained. (Karhunen Loeve expansion)

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

See also

mappings, datasets, pcldc, klm, fisherm, regoptc,