Nonlinear feature extraction with a general criterion function

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Abstract
The optimal nonlinear features for a criterion function of the general formf(D_{l},\cdots,D_{M},K_{1},\cdots,K_{M})are studied, where theD_{j}and the are the conditional first- and second-order moments. The optimal solution is found to be a parametric function of the conditional densities. By imposing a further restriction on the functional dependence offon theK_{j}, the optimal mapping becomes an intuitively pleasing function of the posterior probabilities. Given a finite number of features\psi_{1}(X),\cdots ,\psi_{L}(X), the problem of finding the best linear mappings tomfeatures is next investigated. The resulting optimum mapping is a linear combination of the projections of the posterior probabilities onto the subspace spanned by the\psi_{j}(X). The problem of finding the best single feature and seqnential feature selection is discussed in this framework. Finally, several examples are discussed.

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