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Nonlinear Methods for Dimensionality Reduction

Handbook of Geomathematics

Abstract

The main objective of this handbook paper is to summarize and compare various popular methods and approaches in the research area of dimensionality reduction of high-dimensional data sets, with emphasis on hyperspectral imagery data. In addition, the topics of our discussions will include data preprocessing, data geometry in terms of similarity/dissimilarity, construction of dimensionality reduction kernels, and dimensionality reduction algorithms based on these kernels.

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Author information

Authors and Affiliations

  1. Department of Statistics, Stanford University, Stanford, USA

    Charles K.Chui

Authors
  1. Charles K.Chui
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  2. Jianzhong Wang
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Corresponding author

Correspondence to Charles K.Chui .

Editor information

Editors and Affiliations

  1. Geomathematische Gruppe, TU Kaiserslautern FB Mathematik, Kaiserslautern, Germany

    Willi Freeden

  2. University of Central Florida Dept. Mathematics, Orlando, Florida, USA

    M. Zuhair Nashed

  3. Inst. Analysis, TU Braunschweig FB 1 Mathematik und Informatik, Braunschweig, Germany

    Thomas Sonar

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K.Chui, C., Wang, J. (2013). Nonlinear Methods for Dimensionality Reduction. In: Freeden, W., Nashed, M., Sonar, T. (eds) Handbook of Geomathematics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27793-1_34-2

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  • DOI: https://doi.org/10.1007/978-3-642-27793-1_34-2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Online ISBN: 978-3-642-27793-1

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Chapter history

  1. Latest

    Nonlinear Methods for Dimensionality Reduction
    Published:
    19 February 2015

    DOI: https://doi.org/10.1007/978-3-642-27793-1_34-3

  2. Original

    Nonlinear Methods for Dimensionality Reduction
    Published:
    08 October 2014

    DOI: https://doi.org/10.1007/978-3-642-27793-1_34-2

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