Abstract
In this paper we propose a model based density estimation method which is rooted in Independent Factor Analysis (IFA). IFA is in fact a generative latent variable model, whose structure closely resembles the one of an ordinary factor model but which assumes that the latent variables are mutually independent and distributed according to Gaussian mixtures. From these assumptions, the possibility of modelling the observed data density as a mixture of Gaussian distributions too derives. The number of free parameters is controlled through the dimension of the latent factor space. The model is proved to be a special case of mixture of factor analyzers which is less parameterized than the original proposal by McLachlan and Peel (2000). We illustrate the use of IFA density estimation for supervised classification both on real and simulated data.
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© 2006 Springer Berlin · Heidelberg
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Calò, D.G., Montanari, A., Viroli, C. (2006). Model-based Density Estimation by Independent Factor Analysis. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_19
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DOI: https://doi.org/10.1007/3-540-31314-1_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31313-7
Online ISBN: 978-3-540-31314-4
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