ArticleJournal of machine learning research : JMLR2023
Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering.
Article in Journal of machine learning research : JMLR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Human-inspired sensorimotor controller for dynamic motion adaptation: a study in robotic arms.Frontiers in neurorobotics · 2026Article
- Semi-supervised Bayesian integration of multiple spatial proteomics datasets.PLoS computational biology · 2025Article
- TREE-REGULARIZED BAYESIAN LATENT CLASS ANALYSIS FOR IMPROVING WEAKLY SEPARATED DIETARY PATTERN SUBTYPING IN SMALL-SIZED SUBPOPULATIONS.The annals of applied statistics · 2025Article
- BISON: bi-clustering of spatial omics data with feature selection.Bioinformatics (Oxford, England) · 2025Article
- BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA.The annals of applied statistics · 2025Article
- Bayesian Transfer Learning.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis.Journal of the American Statistical Association · 2025Article
- Robustifying Likelihoods by Optimistically Re-weighting Data.Journal of the American Statistical Association · 2025Article
- Bayesian Clustering via Fusing of Localized Densities.Journal of the American Statistical Association · 2025Article
- Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions.Journal of machine learning research : JMLR · 2025Article
- A clustering effectiveness measurement model based on merging similar clusters.PeerJ. Computer science · 2024Article
- Spectral Clustering, Bayesian Spanning Forest, and Forest Process.Journal of the American Statistical Association · 2024Article
- Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials.Journal of the American Statistical Association · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Bayesian mixture models are widely used for clustering of high-dimensional data with appropriate uncertainty quantification. However, as the dimension of the observations increases, posterior inference often tends to favor too many or too few clusters. This article explains this behavior by studying the random partition posterior in a non-standard setting with a fixed sample size and increasing data dimensionality. We provide conditions under which the finite sample posterior tends to either assign every observation to a different cluster or all observations to the same cluster as the dimension grows. Interestingly, the conditions do not depend on the choice of clustering prior, as long as all possible partitions of observations into clusters have positive prior probabilities, and hold irrespective of the true data-generating model. We then propose a class of latent mixtures for Bayesian clustering (Lamb) on a set of low-dimensional latent variables inducing a partition on the observed data. The model is amenable to scalable posterior inference and we show that it can avoid the pitfalls of high-dimensionality under mild assumptions. The proposed approach is shown to have good performance in simulation studies and an application to inferring cell types based on scRNAseq.
Indexed as
Identifiers
40236516PMC11999651What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.