Paper ID: 2401.16708

Multivariate Beta Mixture Model: Probabilistic Clustering With Flexible Cluster Shapes

Yung-Peng Hsu, Hung-Hsuan Chen

This paper introduces the multivariate beta mixture model (MBMM), a new probabilistic model for soft clustering. MBMM adapts to diverse cluster shapes because of the flexible probability density function of the multivariate beta distribution. We introduce the properties of MBMM, describe the parameter learning procedure, and present the experimental results, showing that MBMM fits diverse cluster shapes on synthetic and real datasets. The code is released anonymously at https://github.com/hhchen1105/mbmm/.

Submitted: Jan 30, 2024