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CSDA
2008

Independent factor discriminant analysis

10 years 12 months ago
Independent factor discriminant analysis
In the general classification context the recourse to the so-called Bayes decision rule requires to estimate the class conditional probability density functions. In this paper we propose a mixture model for the observed variables which is derived by assuming that the data have been generated by an independent factor model. Independent factor analysis is in fact a generative latent variable model whose structure closely resembles the one of 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.
Angela Montanari, Daniela G. Calò, Cinzia V
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where CSDA
Authors Angela Montanari, Daniela G. Calò, Cinzia Viroli
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