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Projection pursuit for exploratory supervised classification

Author

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  • Lee, Eun-Kyung
  • Cook, Dianne
  • Klinke, Sigbert
  • Lumley, Thomas

Abstract

In high-dimensional data, one often seeks a few interesting low-dimensional projections that reveal important features of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group information in the calculation. Hence, they cannot be adequately applied in supervised classification problems to provide low-dimensional projections revealing class differences in the data. We introduce new indices derived from linear discriminant analysis that can be used for exploratory supervised classification.

Suggested Citation

  • Lee, Eun-Kyung & Cook, Dianne & Klinke, Sigbert & Lumley, Thomas, 2005. "Projection pursuit for exploratory supervised classification," SFB 649 Discussion Papers 2005-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2005-026
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    Citations

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    Cited by:

    1. Ursula Laa & Dianne Cook, 2020. "Using tours to visually investigate properties of new projection pursuit indexes with application to problems in physics," Computational Statistics, Springer, vol. 35(3), pages 1171-1205, September.
    2. Adragni, Kofi Placid & Cook, R. Dennis & Wu, Seongho, 2012. "GrassmannOptim: An R Package for Grassmann Manifold Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i05).
    3. Hong Li & Weiwei Zhang & Xiao Xiao & Fei Lun & Yifu Sun & Na Sun, 2023. "Temporal and Spatial Changes of Agriculture Green Development in Beijing’s Ecological Conservation Developing Areas from 2006 to 2016," Sustainability, MDPI, vol. 16(1), pages 1-20, December.
    4. Calo, Daniela G., 2007. "Gaussian mixture model classification: A projection pursuit approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 471-482, September.
    5. Huang, Bei & Cook, Dianne & Wickham, Hadley, 2012. "tourrGui: A gWidgets GUI for the Tour to Explore High-Dimensional Data Using Low-Dimensional Projections," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 49(i06).
    6. Wickham, Hadley & Cook, Dianne & Hofmann, Heike & Buja, Andreas, 2011. "tourr: An R Package for Exploring Multivariate Data with Projections," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i02).

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