Nonsmooth Penalized Clustering via $\ell _{p}$ Regularized Sparse Regression

Clustering has been widely used in data analysis. A majority of existing clustering approaches assume that the number of clusters is given in advance. Recently, a novel clustering framework is proposed which can automatically learn the number of clusters from training data. Based on these works, we propose a nonsmooth penalized clustering model via ℓp (0 <; p <; 1) regularized sparse regression. I...