NDLI: TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models

Content Provider	IEEE Xplore Digital Library
Author	Reddy, C.K. Hsiao-Dong Chiang Rajaratnam, B.
Copyright Year	1979
Abstract	The expectation maximization (EM) algorithm is widely used for learning finite mixture models despite its greedy nature. Most popular model-based clustering techniques might yield poor clusters if the parameters are not initialized properly. To reduce the sensitivity of initial points, a novel algorithm for learning mixture models from multivariate data is introduced in this paper. The proposed algorithm takes advantage of TRUST-TECH (TRansformation Under STability-reTaining Equilibria CHaracterization) to compute neighborhood local maxima on the likelihood surface using stability regions. Basically, our method coalesces the advantages of the traditional EM with that of the dynamic and geometric characteristics of the stability regions of the corresponding nonlinear dynamical system of the log-likelihood function. Two phases, namely, the EM phase and the stability region phase, are repeated alternatively in the parameter space to achieve local maxima with improved likelihood values. The EM phase obtains the local maximum of the likelihood function and the stability region phase helps to escape out of the local maximum by moving toward the neighboring stability regions. Though applied to Gaussian mixtures in this paper, our technique can be easily generalized to any other parametric finite mixture model. The algorithm has been tested on both synthetic and real data sets and the improvements in the performance compared to other approaches are demonstrated. The robustness with respect to initialization is also illustrated experimentally.
Sponsorship	IEEE Computer Society
Page Count	12
File Size	1645854
Starting Page	1146
Ending Page	1157
File Format	PDF
ISSN	01628828
Volume Number	30
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stability Clustering algorithms Nonlinear dynamical systems Unsupervised learning Pattern recognition Maximum likelihood estimation Parameter estimation Testing Robustness Newton method model-based clustering. expectation maximization unsupervised learning finite mixture models dynamical systems stability regions
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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