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Model-based Density Estimation by Independent Factor Analysis

  • Daniela G. Calò
  • Angela Montanari
  • Cinzia Viroli
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Gaussian Mixture Model Factor Analyzer Model Quadratic Discriminant Analysis Allocation Variable Independent Factor Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Daniela G. Calò
    • 1
  • Angela Montanari
    • 1
  • Cinzia Viroli
    • 1
  1. 1.Department of StatisticsUniversity of BolognaItaly

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