Title: Clustering structure based multiple measurement vectors model and its algorithm

Authors: Tijian Cai; Xiaoyu Peng; Xin Xie; Wei Liu; Jia Mo

Addresses: School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, China ' School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, China ' School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, China ' Virtual Reality and Interactive Techniques Institute, East China Jiaotong University, Nanchang, Jiangxi, China ' School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, China

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.

Keywords: compressed sensing; sparse recovery; MMV; multi-measurement vectors; structure sparse.

DOI: 10.1504/IJGUC.2021.120092

International Journal of Grid and Utility Computing, 2021 Vol.12 No.5/6, pp.544 - 553

Received: 17 Feb 2020
Accepted: 08 Jun 2020
Published online: 07 Jan 2022 *

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