1. A method of providing pattern recognition, said method comprising the steps of: inputting a speech pattern into a pattern recognition apparatus; providing minimum Bayes error feature selection via transforming the input pattern to provide a set of features for a classifier which classifies into classes, wherein there is only one feature space transformation for all classes; and providing final features to the classifier, wherein the classifier provides a final output classification result; said transforming step comprising the step of directly minimizing the probability of subsequent misclassification in a projected space of at least one feature; said direct minimizing step comprising: performing a full-covariance gaussian clustering of input records for every class; developing an objective function by way of means, covariances and priors, wherein said objective function either: maximizes an average pairwise divergence and relates it to Bayes error; or directly minimizes an upper bound on Bayes error; optimizing the objective function through gradient decent, wherein all dimensions of a matrix are optimized via optimizing the objective function; wherein the optimizing is carried out over all possible matrices; and wherein the objective function is initialized with an LDA matrix (linear discriminant analysis); upon convergence of the optimization, transforming all the records x into y=θx to produce the at least one final feature where 0 is the LDA matrix; wherein said pattern recognition is speech recognition.
2. The method of claim 1 , further comprising the step of querying whether the optimized objective function converges.