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Projection Pursuit Mixture Density Estimation Mayer Aladjem
 

Summary: Projection Pursuit Mixture Density Estimation
Mayer Aladjem
Department of Electrical and Computer Engineering,
Ben-Gurion University of the Negev,
P.O.B. 653, 84105 Beer-Sheva, Israel
Abstract
In this paper we seek a Gaussian mixture model (GMM) of an n-variate probability
density function. Usually the parameters of GMMs are determined in the original
n-dimensional space by optimizing a maximum likelihood (ML) criterion. A practical
deficiency of this method of fitting GMMs is its poor performance when dealing with
high-dimensional data since a large sample size is needed to match the accuracy that
is possible in low dimensions. We propose a method for fitting the GMM based on
the projection pursuit (PP) strategy. This GMM is highly constrained and hence its
ability to model structure in subspaces is enhanced, compared to a direct ML fitting
of a GMM in high dimensions. Our method is closely related to recently developed
independent factor analysis (IFA) mixture models. The comparisons with ML fitting
of GMM in n-dimensions and IFA mixtures show that the proposed method is an
attractive choice for fitting GMMs using small sizes of training sets.
Index Terms--Multivariate density estimation, Gaussian mixture models, Projection
pursuit, Radial basis functions, Latent variable models, Probabilistic principal com-

  

Source: Aladjem, Mayer - Department of Electrical and Computer Engineering, Ben-Gurion University

 

Collections: Computer Technologies and Information Sciences