1. A method for constructing and using a classification weights matrix A' in a pattern recognition system, comprising the steps of: (a) providing a digital computer with a first set of signals representing a correlation matrix Z between a predetermined set of exemplar feature vectors, each representing the values of a predefined set of F features, and a set of classification target vectors corresponding to said feature vectors; (b) providing said digital computer with a second set of signals representing the autocorrelation matrix W of said set of exemplar feature vectors; (c) determining a classification efficiency of selected subsets of said set of F features, wherein said step of determining the classification efficiency for a given subset comprises the steps of: (c1) utilizing said given subset to construct submatrices of said correlation and autocorrelation matrices; (c2) forming a weights matrix for a linear discriminator, in the vector space of said given subset of features, as a function of said submatrices, and (c3) testing said linear discriminator weights matrix on a predefined set of exemplars to yield the classification efficiency of said given subset; (d) searching a space S of f-element subsets of said set of F features, where f is prespecified and less than F, for an f-element subset having a maximum classification efficiency; (e) selecting one of the f-element subsets in said space, S, as having a maximum classification efficiency as a result of said step of searching; (f) defining said classification weights matrix A' as the product of submatrix Z' of said correlation matrix Z, and an inverse of submatrix W' of said autocorrelation matrix W, where submatrices Z' and W' are constructed as a function of S; (g) capturing a pattern to be recognized using an input device; and (h) performing pattern recognition of the pattern captured with the digital computer using said classification weights matrix A'.