A Variant of the Trace Quotient Formulation for Dimensionality Reduction

  • Peng Wang
  • Chunhua Shen
  • Hong Zheng
  • Zhang Ren
Conference paper

DOI: 10.1007/978-3-642-12297-2_27

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)
Cite this paper as:
Wang P., Shen C., Zheng H., Ren Z. (2010) A Variant of the Trace Quotient Formulation for Dimensionality Reduction. In: Zha H., Taniguchi R., Maybank S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg

Abstract

Due to its importance to classification and clustering, dimensionality reduction or distance metric learning has been studied in depth in recent years. In this work, we demonstrate the weakness of a widely-used class separability criterion—trace quotient for dimensionality reduction—and propose new criteria for the dimensionality reduction problem. The proposed optimization problem can be efficiently solved using semidefinite programming, similar to the technique in [1]. Experiments on classification and clustering are performed to evaluate the proposed algorithm. Results show the advantage of the our proposed algorithm.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Peng Wang
    • 3
  • Chunhua Shen
    • 1
    • 2
  • Hong Zheng
    • 3
  • Zhang Ren
    • 3
  1. 1.Canberra Research LaboratoryNICTACanberraAustralia
  2. 2.Australian National UniversityCanberraAustralia
  3. 3.Beihang UniversityBeijingChina

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