Independent factor discriminant analysis

Angela Montanari, Daniela G. Calo and Cinzia Viroli

Computational Statistics & Data Analysis, 2008, vol. 52, issue 6, 3246-3254

Abstract: In the general classification context the recourse to the so-called Bayes decision rule requires to estimate the class conditional probability density functions. A mixture model for the observed variables which is derived by assuming that the data have been generated by an independent factor model is proposed. Independent factor analysis is in fact a generative latent variable model whose structure closely resembles the one of the ordinary factor model, but it assumes that the latent variables are mutually independent and not necessarily Gaussian. The method therefore provides a dimension reduction together with a semiparametric estimate of the class conditional probability density functions. This density approximation is plugged into the classic Bayes rule and its performance is evaluated both on real and simulated data.

Date: 2008
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