Robust linear representation via exploiting structure prior

Highlights：

• We solve the proposed model by using the Half-Quadratic (HQ) Optimization method, which overcomes the non-smoothness of L1-norm regularizer and the sensitivity of L2-norm regularizer to large outliers. Besides, using the HQ optimization method, many off-the-shelf linear representation methods can be optimized in the same way and thus compared in a fair and comprehensive manner.

• We empirically evaluate the robustness of our proposed method under different noise levels on AR dataset and Extended Yale B dataset. Experimental results demonstrate that our proposed method is useful in dealing with structured noise like occlusions.

摘要

•Given the spatially continuous property of noises like occlusions, this paper proposes a novel method can handle such noise by penalizing the first-order difference of adjacency pixels of that occlusion. By taking advantage of such structure prior, our method is more robust to real-world noises.•We solve the proposed model by using the Half-Quadratic (HQ) Optimization method, which overcomes the non-smoothness of L1-norm regularizer and the sensitivity of L2-norm regularizer to large outliers. Besides, using the HQ optimization method, many off-the-shelf linear representation methods can be optimized in the same way and thus compared in a fair and comprehensive manner.•We empirically evaluate the robustness of our proposed method under different noise levels on AR dataset and Extended Yale B dataset. Experimental results demonstrate that our proposed method is useful in dealing with structured noise like occlusions.