Nonparametric discriminant analysis and nearest neighbor classification

  • Authors:
  • M. Bressan;J. Vitrià

  • Affiliations:
  • Centre de Visió per Computador (CVC) and Departament d'Informàtica, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain;Centre de Visió per Computador (CVC) and Departament d'Informàtica, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2003

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Abstract

Nonparametric discriminant analysis (NDA), opposite to other nonparametric techniques, has received little or no attention within the pattern recognition community. Nearest neighbor classification (NN) instead, has a well established position among other classification techniques due to its practical and theoretical properties. In this paper, we observe that when we seek a linear representation adapted to improve NN performance, what we obtain not surprisingly is quite close to NDA. Since a hierarchy is provided on the extracted features it also serves as a dimensionality reduction technique that preserves NN performance. Experiments evaluate and compare NN classification using our proposed representation against more classical feature extraction techniques.