EM Algorithm for GMM Parameter Estimation with MATLAB Implementation

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Resource Overview

MATLAB source code for estimating Gaussian Mixture Model (GMM) parameters using Expectation-Maximization algorithm, suitable for solving various machine learning problems including clustering, classification, and anomaly detection

Detailed Documentation

The Expectation-Maximization (EM) algorithm provides an iterative method for estimating parameters of Gaussian Mixture Models (GMMs), with available MATLAB implementations. This approach is particularly effective for solving diverse machine learning challenges such as clustering, classification, and anomaly detection. The algorithm alternates between two main steps: the Expectation step (E-step) computes posterior probabilities using current parameter estimates, while the Maximization step (M-step) updates parameters by maximizing the expected log-likelihood function. Key MATLAB functions typically involved include gmdistribution.fit() or custom implementations using matrix operations for covariance calculations and probability density evaluations. The implementation handles initialization strategies, convergence criteria, and regularization techniques to prevent singular covariance matrices.

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