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Large And Complex Data Theory
Escaping The Curse Of Dimensionality In Bayesian Model-based Clustering.

Home
Research Domains
Mathematical Sciences
Statistics
Large And Complex Data Theory
Escaping The Curse Of Dimensionality In Bayesian Model-based Clustering.

Related Experiment Video

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Published on: March 1, 2022

Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering.

Noirrit Kiran Chandra¹, Antonio Canale², David B Dunson³

¹Department of Mathematical Sciences The University of Texas at Dallas Richardson, TX, USA.

Journal of Machine Learning Research : JMLR

|April 16, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Bayesian mixture models struggle with high-dimensional data clustering. This study explains why and introduces Latent Mixtures for Bayesian Clustering (Lamb) to overcome these challenges in dimensionality.

Keywords:

Big data Clustering Dirichlet process Exchangeable partition probability function

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Area of Science:

Statistics
Machine Learning
Computational Biology

Background:

Bayesian mixture models are standard for clustering high-dimensional data.
High dimensionality can lead to incorrect cluster counts in posterior inference.

Purpose of the Study:

To explain the tendency of Bayesian clustering to produce too many or too few clusters in high dimensions.
To propose a novel Bayesian clustering method that addresses high-dimensionality issues.

Main Methods:

Analysis of the random partition posterior in a fixed-sample, increasing-dimension setting.
Development of Latent Mixtures for Bayesian Clustering (Lamb) using low-dimensional latent variables.
Scalable posterior inference techniques for the proposed model.

High dimensional

Latent variables

Main Results:

Identified conditions for posterior inference favoring extreme clustering (all separate or all together) as dimension increases.
Demonstrated that these conditions are prior-choice independent.
Showcased Lamb's ability to avoid high-dimensionality pitfalls under mild assumptions.

Conclusions:

The proposed Latent Mixtures for Bayesian Clustering (Lamb) offers a robust solution for high-dimensional data.
Lamb demonstrates strong performance in simulations and real-world applications like single-cell RNA sequencing data analysis.