Sciweavers

Share
PAMI
2008

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

9 years 7 months ago
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models
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. Th...
Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratn
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2008
Where PAMI
Authors Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratnam
Comments (0)
books