COMMENTS ON LINEAR FEATURE EXTRACTION.

Abstract

The problem considered is that of finding the best linear transformation to reduce a random data vector z to vector of smaller dimension. It is assumed that the original data are Gaussian under either of two hypotheses. The Bhattacharya distance is used to measure the information carried by the transformed data. A compromise solution is obtained for the case in which the data has both different means and different covariances under the alternative hypotheses. (Author)

Document Details

Document Type
Technical Report
Publication Date
Apr 15, 1969
Accession Number
AD0690136

Entities

People

  • D. G. Lainiotis
  • T. L. Henderson

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Covariance
  • Data Science
  • Extraction
  • Feature Extraction
  • Hypotheses
  • Information Science

Fields of Study

  • Mathematics

Readers

  • Neural Network Machine Learning.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms