NDLI: MML-Based Approach for High-Dimensional Unsupervised Learning Using the Generalized Dirichlet Mixture

Content Provider	IEEE Xplore Digital Library
Author	Bouguila, N. Ziou, D.
Copyright Year	2005
Description	Author affiliation: Universite de Sherbrooke (Bouguila, N.)
Abstract	We consider the problem of determining the structure of high-dimensional data, without prior knowledge of the number of clusters. Data are represented by a finite mixture model based on the generalized 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. In addition, the mathematical properties of this distribution allow highdimensional modeling without requiring dimensionality reduction and thus without a loss of information. The number of clusters is determined using the Minimum Message length (MML) principle. Parameters estimation is done by a hybrid stochastic expectation-maximization (HSEM) algorithm. The model is compared with results obtained by other selection criteria (AIC, MDL and MMDL). The performance of our method is tested by real data clustering and by applying it to an image object recognition problem.
Starting Page	53
Ending Page	53
File Size	286090
Page Count	1
File Format	PDF
ISBN	0769523722
ISSN	10636919
DOI	10.1109/CVPR.2005.493
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer vision Parameter estimation Stochastic processes Clustering algorithms Pattern recognition Covariance matrix Mathematical model Face detection Unsupervised learning
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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