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 \url{https://github.com/hhchen1105/mbmm/}.

翻译：本文介绍了多元贝塔混合模型(MBMM)，这是一种用于软聚类的新型概率模型。由于多元贝塔分布具有灵活的概率密度函数，MBMM能够适应多样化的簇形状。我们阐述了MBMM的性质，描述了参数学习过程，并展示了实验结果，表明MBMM在合成数据集和真实数据集上均能拟合多样化的簇形状。相关代码已匿名发布于\url{https://github.com/hhchen1105/mbmm/}。