Sparse representation classification (SRC) and collaborative representation classification (CRC) are the most promising classifiers for classifying high dimensional data. However, they may suffer from outliers and noises, as l(2)-norm on signal fidelity is not effective enough to represent the test sample in that case. Recent studies show that non-convex l(p)-norm minimization can boost the performance of classifiers compared with l(1)- and l(2)-norm minimization in classification. In this paper, we present an improved collaborative representation classification method for the accurate identif...
