Signal classification based on block-sparse tensor representation

Syed Zubair, Wenwu Wang

Abstract: Block sparsity was employed recently in vector/matrix based sparse representations to improve their performance in signal classification. It is known that tensor based representation has potential advantages over vector/matrix based representation in retaining the spatial distributions within the data. In this paper, we extend the concept of block sparsity for tensor representation, and develop a new algorithm for obtaining sparse tensor representations with block structure. We show how the proposed algorithm can be used for signal classification. Experiments on face recognition are provided to demonstrate the performance of the proposed algorithm, as compared with several sparse representation based classification algorithms.