Clustering structure based multiple measurement vectors model and its algorithm. (24th December 2021)

• Record Type: Journal Article
• Authors: Cai, Tijian; Peng, Xiaoyu; Xie, Xin; Liu, Wei; Mo, Jia
• Is Part Of: International journal of grid and utility computing. Volume 12:Number 5/6(2021)
• Page Start: 544
• Page End: 553
• Publication Date: 2021-12-24
• Subjects: compressed sensing -- sparse recovery -- MMV -- multi-measurement vectors -- structure sparse; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

Abstract

Most multi-measurement vector models are based on the ideal assumption of shared sparse structure. However, due to time varying and multiple focuses of complex data, it is often difficult to meet the assumption in reality. Therefore, people have been working hard to utilise various sparse structures to make up for the problem. In this paper, we take the clustering structure of signals into account and propose the Clustering Structure based Multiple Measurement Vectors (CS-MMV) model, which not only utilises clustering characteristic between coefficients but also considers clustering structure within coefficients. Furthermore, we extend a classic algorithm to implement the new model. Experiments on simulation data and two face benchmarks show that the new model is more suitable for complex data with clustered structure, and the extended algorithm can effectively improve the performance of sparse recovery.