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Optimal discrimination linear mapping (Fisher mapping, LDA)


 A Dataset
 N Number of dimensions to map to, N < C, where C is the number of classes  (default: min(C,K)-1, where K is the number of features in A)
 ALF Preserved variance in the pre-whitening step

 W Fisher mapping


Finds a mapping of the labeled dataset A onto an N-dimensional linear  subspace such that it maximises the the between scatter over the within  scatter (also called the Fisher mapping [1 or LDA]). Note that N should be  less than the number of classes in A. If supplied, ALF determines the  preserved variance in the prewhitening step (i.e. removal of insignificant  eigenvectors in the within-scatter, the EFLD procedure [2]), see KLMS.

The resulting mapping is not orthogonal. It may be orthogonalised by ORTH.


[1] K. Fukunaga, Introduction to statistical pattern recognition, 2nd ed., Academic Press, New York, 1990. [2] C. Liu and H. Wechsler, Robust Coding Schemes for Indexing and Retrieval from Large Face Databases, IEEE Transactions on Image Processing, vol. 9, no. 1, 2000, 132-136.

See also

mappings, datasets, nlfisherm, klm, pcam, klms, orth,

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PRTools User Guide

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