Dirichlet Process Mixture Model

Dirichlet Process Mixture Models (DPMMs) are Bayesian non-parametric models used for clustering data, offering the advantage of automatically determining the optimal number of clusters. Current research focuses on improving the efficiency of DPMM inference, particularly through variational methods and distributed computing approaches like collapsed Gibbs sampling, to handle large datasets and streaming data efficiently. These advancements address limitations in computational speed and scalability, enhancing the applicability of DPMMs to diverse fields such as outlier detection, compositional data analysis, and hierarchical forecasting, where their ability to adapt to non-stationary data and complex hierarchical structures is particularly valuable.

Papers

May 2, 2024

Hierarchical mixture of discriminative Generalized Dirichlet classifiers
Elvis Togban, Djemel Ziou
Compositional Data Hierarchical Mixture Dirichlet Process Mixture Model Ordered Matrix Dirichlet Discriminative Classifier

March 18, 2024

Variational Approach for Efficient KL Divergence Estimation in Dirichlet Mixture Models
Samyajoy Pal, Christian Heumann
Variational Method Kullback Leibler Divergence Inverse Divergence Compositional Data Dirichlet Process Mixture Model

January 1, 2024

Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures
Dongwook Kim, Juyeon Park, Hee Cheol Chung, Seonghyun Jeong
Subsampling Method Unsupervised Outlier Detection Random Subspace Dirichlet Process Mixture Model Dirichlet Process Gaussian Mixture

December 18, 2023

Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated Learning
Reda Khoufache, Mustapha Lebbah, Hanene Azzag, Etienne Goffinet, Djamel Bouchaffra
Markov Chain Monte Carlo Bayesian Nonparametric Gibbs Sampler Dirichlet Process Mixture Model

October 13, 2022

Dirichlet process mixture models for non-stationary data streams
Ioar Casado, Aritz Pérez
Variational Inference Non Stationary Bayesian Nonparametric Inference Algorithm Streaming Inference Dirichlet Process Mixture Model

May 24, 2022

Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word
Kazuma Furukawa, Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi
Probabilistic Generative Model Multimodal Classification Symbol Emergence Dirichlet Process Mixture Model

April 21, 2022

Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting
Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen
Deep Probabilistic Model Hierarchical Forecasting Dirichlet Process Mixture Model Coherent Forecast

February 27, 2022

Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
Or Dinari, Oren Freifeld
Open Sampling Data Stream Streaming Data Dirichlet Process Streaming Inference Dirichlet Process Mixture Model