Locality-sensitive Competitive Matrix Regression for Image Classification

Over the past few years, nuclear norm based matrix regression (NMR) has achieved superior performance in the field of image recognition. By employing the nuclear norm as the loss metric to preserve the two-dimensional structural information of error images, NMR exhibits strong robustness against structural noise. However, NMR fails to effectively exploit the category information of training data and ignores the local structural relationships among samples. To overcome the above limitations, we design a new classification method named locality-sensitive competitive matrix regression (LSCMR). Firstly, a competitive representation term is introduced into the objective function of NMR, enforcing training samples from distinct categories to competitively characterize the test sample. To further boost the discriminative ability of representation coefficients, LSCMR incorporates the local structural information between samples, which encourages samples of the correct class to contribute more to the representation process. The alternating direction method of multipliers (ADMM) is utilized to solve the objective function of LSCMR. Experimental results on several public datasets verify the superiority of LSCMR. Specifically, on subset 4 and subset 5 of the Extended Yale B dataset, LSCMR achieves recognition accuracy of 97.37% and 62.18%, respectively, which are 9.21% and 20.02% higher than those of NMR. The source code of LSCMR is available at https://github.com/yinhefeng/LSCMR .