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Regularized Gaussian Mixture Model

A Regularized Gaussian Mixture Model (GMM) adds a small positive constant to the diagonal of each component covariance matrix during the Expectation-Maximization algorithm, preventing singular or near-singular matrices that cause numerical failures when the data are sparse, high-dimensional, or contain near-duplicate observations.

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Regularized Gaussian Mixture Model (Covariance-Regularized EM Clustering)

Taksonomisk metoderegister · ml-model / machine-learning

Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631. · DOI 10.1198/016214502760047131
Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9). Springer. · ISBN 978-0-387-31073-2

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