Please download the dossier by clicking on the dossier button x
×

Pattern recognition method and apparatus for feature selection and object classification

  • US 20060050953A1
  • Filed: 06/20/2005
  • Published: 03/09/2006
  • Est. Priority Date: 06/18/2004
  • Status: Abandoned Application
First Claim
Patent Images

1. A feature selection method for use in a data processing system, wherein the data processing system samples data containing a plurality of features associated with the data, and wherein the data processing system maintains an initial training data set, and wherein the initial training data set includes a plurality of features associated with the initial training data, comprising:

  • (a) sampling the data to derive at least one feature associated with the sampled data;

    (b) synthesizing a feature vector from the at least one feature derived during step (a), wherein the feature vector includes one or more features associated with the data sampled at step (a);

    (c) normalizing the feature vector synthesized at step (b), thereby creating a normalized feature vector;

    (d) performing a non-parametric pair-wise feature test upon the normalized feature vector, wherein adjacent elements in the normalized feature vector are compared in a pair-wise manner thereby generating a plurality of tested features, wherein the tested features represent statistical relationships between the adjacent elements of the normalized feature vector;

    (e) performing correlation processing upon the normalized feature vector, wherein the correlation processing includes;

    (1) sorting the tested features generated in step (d);

    (2) organizing the sorted tested features into a correlation matrix; and

    (3) creating a correlation coefficient matrix corresponding and associated to the correlation matrix, wherein the correlation coefficient matrix includes information indicative of correlation between the tested features; and

    (f) removing a selected feature from a training set if the selected feature is determined to be highly correlated to one or more other features in the training set based on the correlation processing performed in step (e).

View all claims
  • 1 Assignment
Timeline View
Assignment View
    ×
    ×