Double Weighted Low-Rank Representation and Its Efficient Implementation.

To overcome the limitations of existing low-rank representation (LRR) methods, i.e., the error distribution should be known a prior and the leading rank components might be over penalized, this paper proposes a new low-rank representation based model, namely double weighted LRR (DWLRR), using two distinguished properties on the concerned representation matrix. The first characterizes various distributions of the residuals into an adaptively learned weighting matrix for more flexibility of noise resistance. The second employs a parameterized rational penalty as well as a weighting vector s to reveal the importance of different rank components for better approximation to the intrinsic subspace structure. Moreover, we derive a computationally efficient algorithm based on the parallel updating scheme and automatic thresholding operation. Comprehensive experimental results conducted on image clustering demonstrate the robustness and efficiency of DWLRR compared with other state-of-the-art models.