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Statistics and Computing

, Volume 29, Issue 1, pp 43–51 | Cite as

Deep Gaussian mixture models

  • Cinzia ViroliEmail author
  • Geoffrey J. McLachlan
Article
  • 536 Downloads

Abstract

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.

Keywords

Unsupervised classification Mixtures of factor analyzers Stochastic EM algorithm 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Statistical SciencesUniversity of BolognaBolognaItaly
  2. 2.Department of MathematicsUniversity of QueenslandSt. Lucia, BrisbaneAustralia

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