Method of geometric linear discriminant analysis pattern recognition
AssigneeThe United States of America as represented by the National Security Agency
InventorsMark Lawrence Ordowski, Gerard G. L. Meyer
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Abstract
Feature attributes are extracted from an observation space to create feature vectors for each class to be identified. A linear transformation matrix is used to reduce the dimension of the feature vectors. A numerical optimization algorithm maximizes a geometric criterion function in order to calculate the linear transformation matrix, where it exploits the geometry of the class contours of constant density. Next, a classifier based on the feature vectors in a lower dimension is generated and a class is determined for the data represented.
Claims42
A method of geometric linear discriminant analysis pattern recognition, comprising the steps of:
- (a) specifying a plurality of classes for distinguishing a plurality of objects in an observation space, each of said classes having a class label;
- (b) collecting data about said plurality of objects in said observation space, with each of said datum having at least one feature attribute;
- (c) extracting said feature attributes from said data for each of said classes;
- (d) generating at least one d-dimensional feature vector having at least one of said feature attributes, where d is equal to the number of said feature attributes for each of said classes;
- (e) pairing each of said d-dimensional feature vectors with one of said class labels;
- (f) choosing a p number, for reducing the number of said feature attributes in said d-dimensional feature vectors to p, where p is at least one and no more than d−1;
- (g) generating p, d-dimensional geometric discriminant vectors from a subset of said d-dimensional feature vectors;
- (h) generating a p×d geometric matrix from said d-dimensional geometric discriminant vectors;
- (i) generating at least one classifier from said p×d geometric matrix, each of said classifiers defining a decision rule for determining said class of one of said objects;
- (j) applying the decision rule from each of said classifiers for each of said d-dimensional feature vectors for generating said class label for one of said objects;
- and
- (k) determining a performance for each of said classifiers.
Dependencies are inferred from the claim text.