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A Maximum Class Distance Support Vector Machine-Based Algorithm for Recursive Dimension Reduction

  • Zheng Sun
  • Xiaoguang Zhang
  • Dianxu Ruan
  • Guiyun Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)

Abstract

A maximum class distance support vector machine based on the recursive dimension reduction is proposed. This algorithm referring to the concept of fisher linear discriminate analysis is introduced to make the distance between the classes as long as possible along the direction of the discriminate vector, and at the same time a classification hyper-plane with the largest distance between the two classes is achieved. Thus the classification hyper-plane can effectively consist with the distribution of samples, resulting to higher classification accuracy. This paper presents the recursive dimension reduction algorithm and its details. Finally, a simulation illustrates the effectiveness of the presented algorithm.

Keywords

Maximum class distance SVM Recursive dimension reduction 

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References

  1. 1.
    Jin, Z., Yang, J.Y., Tang, Z.M., Hu, Z.S.: A Theorem on the Uncorrelated Optimal Discriminant Vectors. Pattern Recogn. 34, 2041–2047 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Chakraborty, D., Pal, N.R.: A Neuro-fuzzy Scheme for Simultaneous Feature Selection and Fuzzy Rule-based Classification. IEEE Trans. Neural Networks 15, 110–123 (2004)CrossRefGoogle Scholar
  3. 3.
    Cho, H.W.: Identification of Contributing Variables using Kernel-based Discriminant Modeling and Reconstruction. Expert. Sys. Appl. 33, 274–285 (2007)CrossRefGoogle Scholar
  4. 4.
    Romero, E., Sopena, J.M.: Performing Feature Selection with Multilayer Perceptrons. IEEE Trans. Neural Networks 19, 431–441 (2008)CrossRefGoogle Scholar
  5. 5.
    Louw, N., Steel, S.J.: Variable Selection in Kernel Fisher Discriminant Analysis by Means of Recursive Feature Elimination. Comput. Stat. Data Anal. 51, 2043–2055 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Zhang, J.H., Wang, Y.Y.: A Rough Margin based Support Vector Machine. Inf. Sci. 148, 2204–2214 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Publishing House of Electronics Industry, Beijing (2004)zbMATHGoogle Scholar
  8. 8.
    Bian, Z.Q., Zhang, X.G.: Pattern Recognition. Tsinghua University Press, Beijing (2000)Google Scholar
  9. 9.
    Tao, Q., Chu, D.J., Wang, J.: Recursive Support Vector Machines for Dimensionality Reduction. IEEE Trans. Neural Networks 19, 189–193 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zheng Sun
    • 1
  • Xiaoguang Zhang
    • 1
    • 2
  • Dianxu Ruan
    • 1
  • Guiyun Xu
    • 1
  1. 1.College of Mechanical and Electrical EngineeringChina University of Mining and TechnologyXuzhouChina
  2. 2.Department of Electronic Science and EngineeringNanjing UniversityNanjingChina

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