Expectation-maximization algorithms for inference in Dirichlet processes mixture

DOI10.1007/S10044-011-0256-4zbMath1284.68500OpenAlexW2144837664MaRDI QIDQ2444583

Yanyan Li

Publication date: 10 April 2014

Published in: PAA. Pattern Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10044-011-0256-4

zbMATH Keywords

clusteringDirichlet processesexpectation-maximizationfinite mixture models

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Random measures (60G57)

Related Items (1)

Additive mixed models with approximate Dirichlet process mixtures: the EM approach

Uses Software

• PCA-SIFT
• BSDS

Cites Work

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• An introduction to MCMC for machine learning
• Inequalities for Beta and Gamma functions via some classical and new integral inequalities
• Online EM algorithm for mixture with application to Internet traffic modeling
• Optimal Estimation of Amplitude and Phase Modulated Signals
• Finite mixture models
• Gibbs Sampling Methods for Stick-Breaking Priors
• Advanced Lectures on Machine Learning
• Variational inference for Dirichlet process mixtures

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