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CHIME: Clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality

Annals of Statistics, 2019
Unsupervised learning is an important problem in statistics and machine learning with a wide range of applications. In this paper, we study clustering of high-dimensional Gaussian mixtures and propose a procedure, called CHIME, that is based on the EM ...
T. Cai, Jing Ma, Lin-Jun Zhang
semanticscholar   +1 more source

An effective EM algorithm for mixtures of Gaussian processes via the MCMC sampling and approximation

Neurocomputing, 2019
The Mixture of Gaussian Processes (MGP) is a powerful statistical model for characterizing multimodal data, but its conventional Expectation-Maximization (EM) algorithm (Dempster et al., 1977) is computationally intractable because of its time complexity.
Di Wu, Jin-Wen Ma
semanticscholar   +1 more source

EM-Algorithm

Computer Vision, 2021
Patrick J. Laub, Young Lee, T. Taimre
semanticscholar   +2 more sources

Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

, 1977
A. Dempster, N. Laird, D. Rubin
semanticscholar   +1 more source

The EM algorithm and extensions

, 1996
J. Gentle, G. McLachlan, T. Krishnan
semanticscholar   +1 more source

Finding the Observed Information Matrix When Using the EM Algorithm

, 1982
T. Louis
semanticscholar   +1 more source

Mixture densities, maximum likelihood, and the EM algorithm

, 1984
R. Redner, H. Walker
semanticscholar   +1 more source

Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm

, 1979
A. Dawid, A. Skene
semanticscholar   +1 more source

Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm

Biometrics, 1999
B. Muthén, K. Shedden
semanticscholar   +1 more source

Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm

, 1981
R. D. Bock, M. Aitkin
semanticscholar   +1 more source