Deep Gaussian Mixture Models

2017/11/18 by Cinzia Viroli, Geoffrey J. McLachlan, Viroli, Cinzia +1 · 5 citations
Computer Science · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.1711.06929

openalex publication_date 2017/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 are introduced and discussed. A Deep Gaussian Mixture model (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 analysers.

Citations

• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Deep learning in neural networks: An overview

Cited by

• Classification EM-PCA for clustering and embedding
• Rethinking Nonlinearity: Trainable Gaussian Mixture Modules for Modern Neural Architectures
• Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport
• PMODE: Theoretically Grounded and Modular Mixture Modeling
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Related

• Gaussian Mixture Convolution Networks
• Deep Gaussian Mixture Ensembles
• Deep Mixtures of Factor Analysers
• Image Modeling with Deep Convolutional Gaussian Mixture Models
• Deep Gaussian Processes