Patent ID: 7961955

Claim:
A computer-implemented method for extracting discriminately informative features from input patterns, which provide discrimination between two classes, a class-of-interest and a class-other, while reducing the number of features, comprising the steps of: receiving a training set of class-of-interest patterns, a set of unlabeled patterns from an input-data-set, and an estimate of a class-of-interest a priori probability in said input-data-set, said input-data-set being at least one of an image, video or speech data set; selecting elements of a predetermined polynomial function; executing a training stage using said class-of-interest a priori probability, said training set of class-of-interest patterns, and said unlabeled patterns from said input-data-set, said training stage including a step of selecting a set of weights for said polynomial function that ensure a least squares approximation of a class-of-interest posterior distribution function using said polynomial function; classifying said pattern from said input-data-set as being either said class-of-interest or said class-other in accordance with a conditional test defined by an adaptive Bayes decision rule; extracting a predetermined percent of said classified patterns that lie near a decision boundary; locating points lying on said decision boundary using said extracted patterns that lie near said decision boundary; calculating normal vectors to said decision boundary using said points lying on said decision boundary; calculating an effective decision boundary feature matrix; calculating eigenvalues, eigenvectors, and a rank of said effective decision boundary feature matrix; selecting a set of said eigenvectors for use in a feature extraction matrix; and extracting a reduced set of features using said feature extraction matrix, whereby said discriminately informative features are extracted from input patterns which provide discrimination between a class-of-interest and a class-other while reducing the number of features, using only said training set of class-of-interest patterns, and said unlabeled patterns from said input-data-set, and without any a priori knowledge of said class-other.