Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models

DOI10.1007/978-981-15-1960-4_3zbMath1445.62325arXiv1909.05494OpenAlexW3007594395MaRDI QIDQ3305484

Faicel Chamroukhi, Florian Lecocq, Hien Duy Nguyen

Publication date: 7 August 2020

Published in: Communications in Computer and Information Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.05494

zbMATH Keywords

EM algorithmclusteringhigh-dimensional datafeature selectionLassomixtures-of-experts

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistical aspects of big data and data science (62R07)

Cites Work

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• Time series modeling by a regression approach based on a latent process
• Rejoinder to the comments on: \(\ell _{1}\)-penalization for mixture regression models
• Laplace mixture of linear experts
• Robust mixture of experts modeling using the \(t\) distribution
• Pathwise coordinate optimization
• Coordinate descent algorithms for lasso penalized regression
• New estimation and feature selection methods in mixture-of-experts models
• The EM Algorithm and Extensions, 2E

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