Pattern Recognition with GMM + EM + BIC Algorithms

This documentation highlights key pattern recognition algorithms: GMM (Gaussian Mixture Model) + EM (Expectation-Maximization algorithm) + BIC (Bayesian Information Criterion). We recommend running the attached program to deepen your understanding and application of these algorithms. The main program file is: VASceneAnalysisFH7_ICMEFull_2009.m. The implementation demonstrates how GMM models data distributions using multiple Gaussian components, while the EM algorithm iteratively optimizes parameters through E-step (calculating posterior probabilities) and M-step (updating model parameters). BIC serves as a model selection criterion that balances goodness-of-fit with model complexity to prevent overfitting. By executing this program, you can thoroughly study pattern recognition techniques and apply them to your relevant projects for improved results. The code architecture includes data preprocessing, model initialization, iterative EM optimization, and BIC-based model evaluation components.