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PRTools User Guide



Linear classifier using PC expansion on the joint data.

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

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

 W Mapping


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,

PRTools Contents

PRTools User Guide

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