Bayesian sparse convex clustering via global-local shrinkage priors
This is an incremental improvement for researchers in clustering and variable selection, addressing specific limitations in existing sparse convex clustering methods.
The paper tackles the problem of data dependence and reduced estimation accuracy in sparse convex clustering when sample sizes are insufficient by proposing a Bayesian method using global-local shrinkage priors, with effectiveness demonstrated in simulations and real data analysis.
Sparse convex clustering is to cluster 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.