Kernel two-dimensional ridge regression for subspace clustering

Subspace clustering methods have been extensively studied in recent years. For 2-dimensional (2D) data, existing subspace usually convert 2D examples to vectors, which severely damages inherent structural information and relationships of the original data. In this paper, we propose a novel method, named KTRR, for The KTRR provides us with way learn most representative features from data learning representation. particular, performs feature low-dimensional representation construction simultaneously, renders two tasks mutually enhance each other. kernel is introduced enhanced capability capturing nonlinear An efficient algorithm developed its optimization provable decreasing convergent property objective value. Extensive experimental results confirm effectiveness efficiency our method.