A New Linear Dimensionality Reduction Technique Based on Chernoff Distance

  • Luis Rueda
  • Myriam Herrera
Conference paper

DOI: 10.1007/11874850_34

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)
Cite this paper as:
Rueda L., Herrera M. (2006) A New Linear Dimensionality Reduction Technique Based on Chernoff Distance. In: Sichman J.S., Coelho H., Rezende S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. Lecture Notes in Computer Science, vol 4140. Springer, Berlin, Heidelberg

Abstract

A new linear dimensionality reduction (LDR) technique for pattern classification and machine learning is presented, which, though linear, aims at maximizing the Chernoff distance in the transformed space. The corresponding two-class criterion, which is maximized via a gradient-based algorithm, is presented and initialization procedures are also discussed. Empirical results of this and traditional LDR approaches combined with two well-known classifiers, linear and quadratic, on synthetic and real-life data show that the proposed criterion outperforms the traditional schemes.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Luis Rueda
    • 1
  • Myriam Herrera
    • 2
  1. 1.Department of Computer Science and Center for BiotechnologyUniversity of ConcepciónConcepciónChile
  2. 2.Department and Institute of InformaticsNational University of San JuanSan JuanArgentina

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