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Dimension Reduction Methods

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Handbook of Computational Statistics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

One characteristic of computational statistics is the processing of enormous amounts of data. It is now possible to analyze large amounts of high-dimensional data through the use of high-performance contemporary computers. In general, however, several problems occur when the number of dimensions becomes high.

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Author information

Authors and Affiliations

  1. Information Initiative Center, Hokkaido University, Sapporo, Japan

    Masahiro Mizuta

Authors
  1. Masahiro Mizuta
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Correspondence to Masahiro Mizuta .

Editor information

Editors and Affiliations

  1. Dept. Computational & Data, Sciences, George Mason University, University Drive 4400, Fairfax, 22030-4444, Virginia, USA

    James E. Gentle

  2. L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

    Wolfgang Karl Härdle

  3. Dept. Socioinformation, Okayama University, Ridai-cho 1-1, Okayama, 700-0005, Japan

    Yuichi Mori

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Mizuta, M. (2012). Dimension Reduction Methods. In: Gentle, J., Härdle, W., Mori, Y. (eds) Handbook of Computational Statistics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21551-3_22

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