Block-wise weighted sparse representation-based classification

We present a block-wise weighted sparse representation-based classification (BW-SRC) method, an extension of sparse representation-based classification (SRC), useful when the input features can be treated in a block-wise manner. We apply the method, which extracts features (coefficients) embedded in a supervised scheme on three known datasets. We show that, depending on the blocks of features used, our method outperforms linear dictionary learning and sparse coding methods without learning a dictionary. As an additional advantage of our method, the input feature blocks are automatically given an interpretation based on their importance for their ease of representation.