MATLAB Code Implementation of Gaussian Mixture Model
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Detailed Documentation
This is a classic program that implements Gaussian Mixture Model (GMM) specifically designed for MATLAB environment. Gaussian Mixture Model is a probabilistic model widely used in machine learning, data mining, and computer vision applications. It effectively models complex datasets and can be utilized for clustering, anomaly detection, and density estimation tasks. The code implementation includes key components such as Expectation-Maximization (EM) algorithm for parameter estimation, covariance matrix computation, and probability density calculations. By utilizing this source code, you can gain deeper understanding of Gaussian Mixture Model principles and implementation techniques, including initialization methods, convergence criteria, and component selection strategies, which can be applied to your own projects.
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