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.

Model-based Density Estimation by Independent Factor Analysis / D.G. Calò; A. Montanari; C. Viroli. - STAMPA. - (2006), pp. 166-173. (Intervento presentato al convegno 29th Annual Conference of the Gesellschaft für Klassifikation tenutosi a Magdeburg nel March 9-11, 2005).

Model-based Density Estimation by Independent Factor Analysis

CALO', DANIELA GIOVANNA;MONTANARI, ANGELA;VIROLI, CINZIA
2006

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.
2006
From Data and Information Analysis to Knowledge Engineering, Studies in Classification, Data Analysis, and Knowledge Organization
166
173
Model-based Density Estimation by Independent Factor Analysis / D.G. Calò; A. Montanari; C. Viroli. - STAMPA. - (2006), pp. 166-173. (Intervento presentato al convegno 29th Annual Conference of the Gesellschaft für Klassifikation tenutosi a Magdeburg nel March 9-11, 2005).
D.G. Calò; A. Montanari; C. Viroli
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/26475
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact