Bayesian inference for infinite asymmetric Gaussian mixture with feature selection-Reference-Cited by-同舟云学术

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Bayesian inference for infinite asymmetric Gaussian mixture with feature selection

Published:2021-02-02 Issue:8 Volume:25 Page:6043-6053
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Song Ziyang,Ali Samr^ORCID,Bouguila Nizar

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-021-05598-4.pdf

Reference44 articles.

1. Adams S, Beling PA (2017) A survey of feature selection methods for gaussian mixture models and hidden markov models. Artif Intell Rev 52:1739–1779

2. Andonovski G, Mušič G, Blažič S, Škrjanc I (2018) Evolving model identification for process monitoring and prediction of non-linear systems. Eng Appl Artif Intell 68:214–221

3. Antoniak CE (1974) Mixtures of dirichlet processes with applications to bayesian nonparametric problems. Ann Statist 2(6):1152–1174

4. Blei DM, Jordan MI (2006) Variational inference for dirichlet process mixtures. Bayesian Anal 1(1):121–143

5. Bouguila N (2009) A model-based approach for discrete data clustering and feature weighting using MAP and stochastic complexity. IEEE Trans Knowl Data Eng 21(12):1649–1664

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1. Refining Nonparametric Mixture Models with Explainability for Smart Building Applications;2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC);2023-10-01

2. Bayesian Model and Feature Selection in Asymmetric Generalized Gaussian Mixtures;2022 IEEE International Conference on Industrial Technology (ICIT);2022-08-22

3. Feature Saliencies in Asymmetric Hidden Markov Models;IEEE Transactions on Neural Networks and Learning Systems;2022

4. Hidden Markov Model-Based Video Recognition for Sports;Advances in Mathematical Physics;2021-12-20

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