Combining Variable Selection with Dimensionality Reduction

Abstract

This paper bridges the gap between variable selection methods (e.g.. Pearson coefficients. KS test) and dimensionality reduction algorithms (e.g.. PCA. LDA). Variable selection algorithms encounter difficulties dealing with highly correlated data. since many features are similar in quality. Dimensionality reduction algorithms tend to combine all variables and cannot select a subset of significant variables. Our approach combines both methodologies by applying variable selection followed by dimensionality reduction. This combination makes sense only when using the same utility function in both stages. which we do. The resulting algorithm benefits from complex features as variable selection algorithms do. and at the same time enjoys the benefits of dimensionality reduction.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2005
Accession Number
ADA454990

Entities

People

  • Lior Wolf
  • Stanley Bileschi

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Covariance
  • Data Science
  • Databases
  • Detection
  • Dimensionality Reduction
  • Discriminant Analysis
  • Eigenvalues
  • Eigenvectors
  • Feature Selection
  • Information Science
  • Machine Learning
  • Matrix Theory
  • Optimization
  • Recognition
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Image Processing and Computer Vision.
  • Regression Analysis.