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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Podrobná bibliografie
Vydáno v:	Statistics and computing Ročník 29; číslo 1; s. 43 - 51
Hlavní autoři:	Viroli, Cinzia, McLachlan, Geoffrey J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.01.2019 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	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