A weighted competitive nonnegative representation method for image classification

We present a weighted competitive nonnegative representation (WCNR) method. Specifically, WCNR introduces a competitive term, which leverages the competitive ability of training data from distinct classes to strengthen the connection between the representation and classification phases. In addition, WCNR incorporates a weight constraint. The weight constraint of each class imposed on the representation coefficients endows similar classes with more representation contributions, which boosts the discriminative power of the representation coefficients. To assess the classification performance of WCNR, extensive experiments are carried out on benchmarking datasets. Experimental results confirm that WCNR exceeds the state-of-the-art representation-based classification methods (RBCM) and also surpasses several deep learning approaches. The MATLAB code of WCNR is available at https://github.com/yinhefeng/WCNR.