Projection Pursuit for Exploratory Supervised Classification
AbstractIn 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.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2005-026.
Length: 26 pages
Date of creation: May 2005
Date of revision:
Data mining; Exploratory multivariate data analysis; Gene expression data; Discriminant analysis;
Find related papers by JEL classification:
- C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
- C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-10-29 (All new papers)
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- Hadley Wickham & Dianne Cook & Heike Hofmann & Andreas Buja, . "tourr: An R Package for Exploring Multivariate Data with Projections," Journal of Statistical Software, American Statistical Association, vol. 40(i02).
- Calo, Daniela G., 2007. "Gaussian mixture model classification: A projection pursuit approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 471-482, September.
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