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.
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Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number
SFB649DP2005-026.
Find related papers by JEL classification: C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Other C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
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301, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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