An efficient joint compression and sparsity estimation matching pursuit algorithm for artificial intelligence application

作者：Yao, Shihong; Zheng, Zhigao^*; Wang, Tao^*; Guan, Qingfeng

来源：Future Generation Computer Systems-The International Journal of eScience, 2018, 86: 603-613.

DOI：10.1016/j.future.2018.04.039

• Compressed sensing
• Signal reconstruction
• Chaotic measurement matrix
• Sparsity estimation
• Restricted isometry property

Measurement matrix and signal reconstruction algorithm are the key factors influencing the performance of signal reconstruction. However, the reconstruction performance of the existing matching pursuit algorithms, the most popular reconstruction algorithms, is closely related to the signal sparsity, which is hard to determinate apriori. As well, the researches on the reconstruction algorithms are developed independently with the design of measurement matrix. So, in this paper, we originally conduct a joint study of design of measurement matrix and signal reconstruction algorithm. RIP criterion is used to quantitatively analyze the relationship between the signal sparsity and the measurement matrix, and then an efficient joint compression and sparsity estimation matching pursuit OCSEMP) algorithm is proposed. JCSEMP algorithm constructs a chaotic measurement matrix, a sparsity estimation algorithm based on the chaotic measurement matrix, and a variable atom selection criterion which use the variation between the residuals to adaptively adjust the number of atoms to select. Experimental results demonstrate that this algorithm can provide a better reconstruction performance and a lower reconstruction period.

• 出版日期2018-9
• 单位华中科技大学; 中国地质大学（北京）; 武汉大学