Margin Based Dimensionality Reduction and Generalization

Abstract

Linear discriminant analysis (LDA) for dimension reduction has been applied to a wide variety of problems such as face recognition. However, it has a major computational difficulty when the number of dimensions is greater than the sample size. In this paper, we propose a margin based criterion for linear dimension reduction that addresses the above problem associated with LDA. We establish an error bound for our proposed technique by showing its relation to least squares regression. In addition, there are well established numerical procedures such as semi-definite programming for optimizing the proposed criterion. We demonstrate the efficacy of our proposal and compare it against other competing techniques using a number of examples.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2010
Accession Number
ADA530724

Entities

People

  • Guna Seetharaman
  • Jing Peng
  • Stefan Robila
  • Wei Fan

Organizations

  • Air Force Research Laboratory

Tags

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Computer Programming
  • Computer Science
  • Data Mining
  • Data Science
  • Data Sets
  • Dimensionality Reduction
  • Discriminant Analysis
  • Eigenvalues
  • Eigenvectors
  • Feature Extraction
  • Information Science
  • Machine Learning
  • Optimization
  • Recognition

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Neural Network Machine Learning.
  • Operations Research
  • Regression Analysis.

Technology Areas

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