Sparse convex clustering is to group observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted \(L_1\) norm is usually employed for the regularization term in sparse convex clustering, its use increases the dependence on the data and reduces the estimation accuracy if the sample size is not sufficient. To tackle these problems, this paper proposes a Bayesian sparse convex clustering method based on the ideas of Bayesian lasso and global-local shrinkage priors. We introduce Gibbs sampling algorithms for our method using scale mixtures of normal distributions. The effectiveness of the proposed methods is shown in simulation studies and a real data analysis.

稀疏凸聚类是在凸聚类的框架内对观察进行分组并同时进行变量选择。尽管在稀疏凸聚类中通常将加权\（L_1 \）范数用于正则化项，但如果样本量不足，则使用它会增加对数据的依赖性并降低估计精度。针对这些问题，本文提出了一种基于贝叶斯套索思想和全局局部收缩先验的贝叶斯稀疏凸聚类方法。我们使用正态分布的比例混合为我们的方法引入吉布斯采样算法。仿真研究和实际数据分析表明了所提出方法的有效性。