GMM Implementation for Graduation Project
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In my graduation project, I implemented a Gaussian Mixture Model (GMM) using MATLAB. The program underwent extensive testing and demonstrated stable performance. GMM is a widely-used machine learning algorithm for data modeling and classification tasks. My implementation involved key components such as expectation-maximization (EM) algorithm for parameter estimation, covariance matrix calculations, and probability density function evaluations. The MATLAB code efficiently handles multi-dimensional data clustering through iterative optimization of model parameters including means, covariances, and mixture weights. This implementation formed a critical part of my research methodology, significantly contributing to the project's outcomes by providing accurate probabilistic modeling of complex datasets.
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