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Evolutionary computation-based kernel optimal component analysis for pattern recognition

Published: 07 July 2007 Publication History

Abstract

Kernel methods are mathematical tools that provide higher dimensional representation of given data set in feature space for pattern recognition and data analysis problems. Optimal Component Analysis (OCA) [4] poses the problem of finding an optimal linear representation. In this paper we present the results of six kernel functions and their respective performance for Evolutionary Computation-based kernel OCA on the Pima Indian Diabetes database. Empirical results show that we outperform existing techniques on this database.

References

[1]
M. Aizerman, E. Braverman, and L. Rozonoer, "Theoretical foundations of the potential function method in pattern recognition learning". Automation and Remote Control 25: 821--837 (1964).
[2]
J. Isaacs, S. Foo, and A. Meyer-Baese, "Novel Kernels and Kernel PCA for Pattern Recognition", Proceedings of the 7th IEEE International Symposium on Computational Intelligence in Robotics and Automation.
[3]
B. Schölkopf and A. J. Smola: Learning with Kernels. MIT Press,Cambridge, MA, 2002.
[4]
Q. Zhang and X. Liu, "Kernel Optimal Component Analysis," proceedings of the 2004 IEEE Computer Vision and Pattern Recognition Workshops.

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  • (2009)Stochastic orthogonal and nonorthogonal subspace basis pursuitProceedings of the 2009 international joint conference on Neural Networks10.5555/1704555.1704630(2496-2501)Online publication date: 14-Jun-2009

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  1. Evolutionary computation-based kernel optimal component analysis for pattern recognition

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2007

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    Author Tags

    1. component analysis
    2. evolutionary computation
    3. kernels

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2009)Stochastic orthogonal and nonorthogonal subspace basis pursuitProceedings of the 2009 international joint conference on Neural Networks10.5555/1704555.1704630(2496-2501)Online publication date: 14-Jun-2009

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