Sparse linear representations for recognition

Recently, it has been argued that sparse coding is an important principle for recognition, which has been used effectively to derive filters with desirable properties. However there is no effective algorithm to link the sparse coding principle to the recognition performance. Our experiments show that commonly used sparse bases often give worse recognition performance compared to other linear bases. In this paper, we propose a criterion consisting of weighted combination of recognition performance and sparseness. Using a Monte Carlo simulated annealing algorithm, we obtain linear bases with sparse representation as well as good recognition performance. We also find an interesting relationship among commonly used linear representations by comparing their sparseness and recognition performance.