Computer Science ›› 2018, Vol. 45 ›› Issue (3): 258-262.doi: 10.11896/j.issn.1002-137X.2018.03.041

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Sparse Representation Classification Model Based on Non-shared Multiple Measurement Vectors

CAI Ti-jian, FAN Xiao-ping, CHEN Zhi-jie and LIAO Zhi-fang   

  • Online:2018-03-15 Published:2018-11-13

Abstract: Simultaneous sparse reconstruction of multiple measurement vectors(MMV) requires that the multiple mea-surement signals share the same sparse structure.However,it is difficult to get the measurement signals exactly sharing same sparse structure in practical applications.In order to reduce the influence of non-shared sparse structure on simultaneous sparse reconstruction of MMV model,this paper proposed a method to improve simultaneous sparse reconstruction algorithms belonging to greedy series.At each iteration,the method does not require that each measurement vector chooses the same representation atoms,but requires selecting representation atoms in the same class.The improved algorithm can be used for sparse representation classification of non-shared multiple measurement vectors.Experiments on simulated data and standard face database show that the improved model can effectively improve the performance of sparse representation classification.

Key words: Compressed sensing,Multiple measurement vector,Shared sparse structure,Sparse representation classification

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