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Relative Projection Pursuit and its Application

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Book cover Classification, Clustering, and Data Mining Applications

Abstract

In this paper, we propose a new method of projection pursuit, relative projection pursuit (RPP), which finds ‘interesting‘ low dimensional spaces different from reference data sets predefined by the user. In addition, as an application of the method, we develop a new dimension reduction method: sliced inverse regression with relative projection pursuit.

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References

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Author information

Authors and Affiliations

  1. Hokkaido University, Japan

    Masahiro Mizuta & Shintaro Hiro

Authors
  1. Masahiro Mizuta
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  2. Shintaro Hiro
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Editor information

Editors and Affiliations

  1. Leanna House Institute of Statistics and Decision Sciences, Duke University, 27708, Durham, NC, USA

    David Banks

  2. Department of Mathematics, Illinois Institute of Technology, 10 West 32nd Street, 60616-3793, Chicago, IL, USA

    Frederick R. McMorris

  3. Faculty of Management, Rutgers University, 180 University Avenue, 07102-1895, Newark, NJ, USA

    Phipps Arabie

  4. Institute of Decision Theory, University of Karlsruhe, Kaiserstr. 12, 76128, Karlsruhe, Germany

    Wolfgang Gaul

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© 2004 Springer-Verlag Berlin Heidelberg

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Mizuta, M., Hiro, S. (2004). Relative Projection Pursuit and its Application. In: Banks, D., McMorris, F.R., Arabie, P., Gaul, W. (eds) Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17103-1_13

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  • DOI: https://doi.org/10.1007/978-3-642-17103-1_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22014-5

  • Online ISBN: 978-3-642-17103-1

  • eBook Packages: Springer Book Archive

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