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Local Linear Logistic Discriminant Analysis with Partial Least Square Components

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Advanced Data Mining and Applications (ADMA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4093))

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Abstract

We propose a nonparametric local linear logistic approach based on local likelihood in multi-class discrimination. The combination of the local linear logistic discriminant analysis and partial least square components yields better prediction results than the conventional statistical classifiers in case where the class boundaries have curvature. We applied our method to both synthetic and real data sets.

This study was financially supported by research fund of Chonnam National University in 2003.

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Authors and Affiliations

  1. Department of Statistics, Chonnam National University, Gwangju, 500-757, South Korea

    Jangsun Baek & Young Sook Son

Authors
  1. Jangsun Baek
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  2. Young Sook Son
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electronic Engineering, The University of Queensland, Queensland, Australia

    Xue Li

  2. University of Alberta, Canada

    Osmar R. Zaïane

  3. Northwest Polytechnical University, China

    Zhanhuai Li

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© 2006 Springer-Verlag Berlin Heidelberg

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Baek, J., Son, Y.S. (2006). Local Linear Logistic Discriminant Analysis with Partial Least Square Components. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_64

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  • DOI: https://doi.org/10.1007/11811305_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37025-3

  • Online ISBN: 978-3-540-37026-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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