Title :
Signal classification based on block-sparse tensor representation
Author :
Zubair, Syed ; Wenwu Wang
Author_Institution :
Centre for Vision, Speech & Signal Process., Univ. of Surrey, Guildford, UK
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.
Keywords :
face recognition; signal classification; tensors; block sparse tensor; block sparsity; face recognition; signal classification; spatial distribution; Dictionaries; Digital signal processing; Indexes; Signal processing algorithms; Sparse matrices; Tensile stress; Vectors; Block Sparse Representations; Classification; Dictionary Learning; Tensor Factorization;
Conference_Titel :
Digital Signal Processing (DSP), 2014 19th International Conference on
Conference_Location :
Hong Kong
DOI :
10.1109/ICDSP.2014.6900687