2018/08/10 by Ketong Wang, Michael D. Porter · 3 citations
Computer Science · Mathematics · #Advanced Clustering Algorithms Research #Artificial intelligence #Bayesian Methods and Mixture Models #Bayesian inference #Bayesian probability #Cluster analysis #Combinatorics #Computer science #Gaussian Processes and Bayesian Inference #Inference #Markov chain Monte Carlo #Mathematics #Matrix decomposition #Non-negative matrix factorization #Partition (number theory) #Pattern recognition (psychology) #stat.ME
paper · pdf · doi:10.1016/j.csda.2018.08.002
published in Computational Statistics & Data Analysis 128, 395-411 (Elsevier BV)
openalex publication_date 2018/08/10 · arxiv created 2018/09/20 · arxiv updated 2018/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Bayesian model-based clustering is a widely applied procedure for discovering groups of related observations in a dataset. These approaches use Bayesian mixture models, estimated with MCMC, which provide posterior samples of the model parameters and clustering partition. While inference on model parameters is well established, inference on the clustering partition is less developed. A new method is developed for estimating the optimal partition from the pairwise posterior similarity matrix generated by a Bayesian cluster model. This approach uses non-negative matrix factorization (NMF) to provide a low-rank approximation to the similarity matrix. The factorization permits hard or soft partitions and is shown to perform better than several popular alternatives under a variety of penalty functions.