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...

• Type: Journal
• Year: 2008
• Where: PAMI
• Authors: Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratnam
• Keywords: Finite Mixture Model, Mixture Models, PAMI 2008, Stability Regions