Article ID Journal Published Year Pages File Type
416623 Computational Statistics & Data Analysis 2007 12 Pages PDF
Abstract

Gaussian mixture models (GMM) are commonly employed in nonparametric supervised classification. In high-dimensional problems it is often the case that information relevant to the separation of the classes is contained in a few directions. A GMM fitting procedure oriented to supervised classification is proposed, with the aim of reducing the number of free parameters. It resorts to projection pursuit as a dimension reduction method and combines it with GM modelling of class-conditional densities. In its derivation, issues regarding the forward and backward projection pursuit algorithms are discussed. The proposed procedure avoids the “curse of dimensionality”, is able to model structure in subspaces and regularizes the classification model. Its performance is illustrated on a simulation experiment and on a real data set, in comparison with other GMM-based classification methods.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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