Deep Gaussian mixture models

Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, deep Gaussian mixture models (DGMM) are introduced and discussed. A DGMM is a network of multiple layers of latent variables, where...

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Veröffentlicht in:Statistics and computing Jg. 29; H. 1; S. 43 - 51
Hauptverfasser: Viroli, Cinzia, McLachlan, Geoffrey J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2019
Springer Nature B.V
Schlagworte:
Analyzers
Artificial Intelligence
Machine learning
Mathematics and Statistics
Probabilistic models
Probability and Statistics in Computer Science
Regression analysis
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Mixtures of factor analyzers
Stochastic EM algorithm
Unsupervised classification
ISSN:0960-3174, 1573-1375
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Zusammenfassung:Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, deep Gaussian mixture models (DGMM) are introduced and discussed. A DGMM is a network of multiple layers of latent variables, where, at each layer, the variables follow a mixture of Gaussian distributions. Thus, the deep mixture model consists of a set of nested mixtures of linear models, which globally provide a nonlinear model able to describe the data in a very flexible way. In order to avoid overparameterized solutions, dimension reduction by factor models can be applied at each layer of the architecture, thus resulting in deep mixtures of factor analyzers.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-017-9793-z