Local Weighted Representation Based Matrix Regression Classifier and Face Recognition

Abstract

Nuclear-norm-based matrix regression (NMR) approaches utilize the nuclear norm for error term characterization, which strengthens the robustness of algorithm. However, NMR ignores the differences between samples from different classes, which leads to a poor feature representation. Moreover, NMR does not consider variations within different class, which affects the classification performance when classes are not homogeneous. To solve above problems, a local weighted representation-based matrix regression method (LWMR) is proposed. LWMR method solves two issues of current NMR methods that are based on nuclear norm. First, LWMR utilizes the prior distance information between test and training samples as weights, which improves the inter-class separation. Second, LWMR creates a new dictionary by averaging samples within different class and choosing the best representative sample for each class, which reduces the dictionary size and complexity. Experimental results on four widely used datasets demonstrate that LWMR method has faster calculation speed and better image performance than other regression models.