Independent factor discriminant analysis

Angela Montanari, Daniela G. Calò, Cinzia Viroli
<span title="">2008</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/akgkzs3bjzgpdixvnrfpwqkvki" style="color: black;">Computational Statistics &amp; Data Analysis</a> </i> &nbsp;
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
more &raquo; ... iables 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.csda.2007.09.026">doi:10.1016/j.csda.2007.09.026</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vicfovotxjcsvkniqvlqcr26cm">fatcat:vicfovotxjcsvkniqvlqcr26cm</a> </span>
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