CHERNOFFM

Suboptimal discrimination linear mapping (Chernoff mapping)

   W = CHERNOFFM(A,N,R)

Input
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)
R	Regularization variable, 0 <= r <= 1, default is r = 0, for r = 1 the  Chernoff mapping is (should be) equal to the Fisher mapping

Output
W	Chernoff mapping

Description

Finds a mapping of the labeled dataset A onto an N-dimensional linear  subspace such that it maximizes the heteroscedastic Chernoff criterion  (also called the Chernoff mapping).

Reference(s)

M. Loog and R.P.W. Duin, Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion, IEEE Transactions on pattern analysis and machine intelligence, vol. PAMI-26, no. 6, 2004, 732-739.

See also

mappings, datasets, fisherm, nlfisherm, klm, pca,