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Adaptive bayes feature extraction

  • US 7,961,955 B1
  • Filed: 01/28/2008
  • Issued: 06/14/2011
  • Est. Priority Date: 01/28/2008
  • Status: Expired due to Fees
First Claim
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1. 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.

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