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Modeling by Combining Dimension Reduction and L2Boosting

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Intelligent Computing Technology (ICIC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7389))

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Abstract

Dimension reduction techniques are widely used in high dimensional modeling. The two stage approach, first making dimension reduction and then applying existing regression or classification method, is commonly used in practice. However, an important issue is that when two stage approach can lead to consistent estimate. In this paper, we focus on L2boosting and discuss the consistency of the two stage method-dimension reduction based L2boosting (briey DRL2B). We establish the conditions under which DRL2B method results in consistent estimate. This theoretical finding provides some useful guideline for practical application. In addition, we propose an iterative DRL2B approach and make some simulation study. Simulation results shows that iterative DRL2B method has good performance

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Authors and Affiliations

  1. School of Mathematics and System Science, Beihang University, LMIB of the Ministry of Education, Beijing, China

    Junlong Zhao

Authors
  1. Junlong Zhao
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Editors and Affiliations

  1. School of Electronics and Information Engineering, Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    Changjun Jiang

  3. Electrical and Electronics Department, Polytechnic of Bari, Via Orabona, 4, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  4. Faculty of Engineering, District University Francisco José de Caldas, Cra. 7a No. 40-53, Fifth Floor, Bogotá, Colombia

    Juan Carlos Figueroa

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© 2012 Springer-Verlag Berlin Heidelberg

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Zhao, J. (2012). Modeling by Combining Dimension Reduction and L2Boosting. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_30

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  • DOI: https://doi.org/10.1007/978-3-642-31588-6_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31587-9

  • Online ISBN: 978-3-642-31588-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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