Classification Improvement by Optimal Dimensionality Reduction when Training Sets are of Small Size.

Abstract

When the sizes of the training sets are small, classification in a subspace of the original data space may give rise to a smaller probability of error than the classification in the data space itself. This is because the gain in the accuracy of estimation of the likelihood functions used in classification in the lower dimensional space (subspace) offsets the loss of information associated with dimensionality reduction (feature extraction). To test this conjecture, a computer simulation was performed. A number of pseudo-random training and data vectors were generated from two four-dimensional Gaussian classes.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1976
Accession Number
ADA032672

Entities

People

  • D. L. Van Rooy
  • R. J. P. Defigueiredo
  • S. A. Starks

Organizations

  • Rice University

Tags

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Classification
  • Computer Simulations
  • Contracts
  • Covariance
  • Dimensionality Reduction
  • Electrical Engineering
  • Engineering
  • Errors
  • Feature Extraction
  • Four Dimensional
  • Pattern Recognition
  • Recognition
  • Security
  • Simulations
  • Training

Readers

  • Approximation Theory.
  • Instructional Design and Training Evaluation.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space