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Found 5 papers, 2 papers with code
Mixed data Deep Gaussian Mixture Model: A clustering model for mixed datasets
In this work we introduce a multilayer architecture model-based clustering method called Mixed Deep Gaussian Mixture Model (MDGMM) that can be viewed as an automatic way to merge the clustering performed separately on continuous and non-continuous data.
Deep Mixtures of Unigrams for uncovering Topics in Textual Data
Mixtures of Unigrams are one of the simplest and most efficient tools for clustering textual data, as they assume that documents related to the same topic have similar distributions of terms, naturally described by Multinomials.
Classifying textual data: shallow, deep and ensemble methods
This paper focuses on a comparative evaluation of the most common and modern methods for text classification, including the recent deep learning strategies and ensemble methods.
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.
Infinite Mixtures of Infinite Factor Analysers
Factor-analytic Gaussian mixture models are often employed as a model-based approach to clustering high-dimensional data.
Methodology
