Abstract
Compressed sensing (CS)-based cross-and-bouquet (CAB) model was proposed by J. Wright et al. to reduce the complexity of sparse error correcting. For the sake of leading to better performance of CS-based decoding for the CAB model, an algorithm is proposed in this paper for constructing a well-designed projection matrix to minimize the average measures of mutual coherence. One was proposed by M. Elad. Another is defined in this paper for higher dimensional cases. Using the equivalent dictionary, the dimensionality is reduced. Also high-dimensional singular value decomposition (SVD) is avoided in the procedure of constructing a well-designed projection matrix. The high-dimensional CAB model of sparse error correcting can be solved by the proposed algorithm without computational difficulty. The validity of the proposed method is illustrated by decoding experiments in high-dimensional cases.
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Acknowledgements
This work is supported partially by Guangdong Province and National ministry of education IAR projection (Grant No. 2012B091100331) and NSFC-Guangdong union project (Grant: U0835003).
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Fu, Y., Zhang, Q. & Xie, S. Compressed Sensing for Sparse Error Correcting Model. Circuits Syst Signal Process 32, 2371–2383 (2013). https://doi.org/10.1007/s00034-013-9574-7
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DOI: https://doi.org/10.1007/s00034-013-9574-7