A discriminant analysis for undersampled data

  • Authors:
  • Matthew Robards;Junbin Gao;Philip Charlton

  • Affiliations:
  • Charles Sturt University Wagga, Wagga, NSW;Charles Sturt University, Bathurst, NSW;Charles Sturt University Wagga, Wagga, NSW

  • Venue:
  • AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

One of the inherent problems in pattern recognition is the undersampled data problem, also known as the curse of dimensionality reduction. In this paper a new algorithm called pairwise discriminant analysis (PDA) is proposed for pattern recognition. PDA, like linear discriminant analysis (LDA), performs dimensionality reduction and clustering, without suffering from undersampled data to the same extent as LDA.