NDLI: A hybrid SEM algorithm for high-dimensional unsupervised learning using a finite generalized Dirichlet mixture

Content Provider	IEEE Xplore Digital Library
Author	Bouguila, N. Ziou, D.
Copyright Year	1992
Abstract	This paper applies a robust statistical scheme to the problem of unsupervised learning of high-dimensional data. We develop, analyze, and apply a new finite mixture model based on a generalization of the Dirichlet distribution. The generalized Dirichlet distribution has a more general covariance structure than the Dirichlet distribution and offers high flexibility and ease of use for the approximation of both symmetric and asymmetric distributions. We show that the mathematical properties of this distribution allow high-dimensional modeling without requiring dimensionality reduction and, thus, without a loss of information. This makes the generalized Dirichlet distribution more practical and useful. We propose a hybrid stochastic expectation maximization algorithm (HSEM) to estimate the parameters of the generalized Dirichlet mixture. The algorithm is called stochastic because it contains a step in which the data elements are assigned randomly to components in order to avoid convergence to a saddle point. The adjective "hybrid" is justified by the introduction of a Newton-Raphson step. Moreover, the HSEM algorithm autonomously selects the number of components by the introduction of an agglomerative term. The performance of our method is tested by the classification of several pattern-recognition data sets. The generalized Dirichlet mixture is also applied to the problems of image restoration, image object recognition and texture image database summarization for efficient retrieval. For the texture image summarization problem, results are reported for the Vistex texture image database from the MIT Media Lab
Sponsorship	IEEE Signal Processing Society
Page Count	12
File Size	2509956
Starting Page	2657
Ending Page	2668
File Format	PDF
ISSN	10577149
Volume Number	15
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Unsupervised learning Stochastic processes Image databases Robustness Data analysis Mathematical model Parameter estimation Convergence Testing Image restoration Vistex Clustering correlogram expectation maximization (EM) finite mixture models generalized Dirichlet high-dimensional data hybrid stochastic expectation maximization algorithm (HSEM) image database summarization image object recognition image restoration maximum likelihood (ML) SEM
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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