In EDS ansehen

Approximate Message Passing for Structured Affine Rank Minimization Problem

Gespeichert in:
Bibliographische Detailangaben
Titel: Approximate Message Passing for Structured Affine Rank Minimization Problem
Autoren: Yangqing Li, Changchuan Yin, Wei Chen, Zhu Han
Quelle: IEEE Access, Vol 5, Pp 10093-10107 (2017)
Verlagsinformationen: IEEE, 2017.
Publikationsjahr: 2017
Bestand: LCC:Electrical engineering. Electronics. Nuclear engineering
Schlagwörter: Affine rank minimization (ARM), structured low-rank matrix recovery, approximate message passing (AMP), belief propagation, graphical models, sum-product algorithm (SPA), Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Beschreibung: The topic of the rank minimization problem with affine constraints has been well studied in recent years. However, in many applications the data can exhibit other structures beyond simply being low rank. For example, images and videos present complex spatio-temporal structures, which are largely ignored by current affine rank minimization (ARM) methods. In this paper, we propose a novel approximate message passing (AMP)-based approach that is capable of capturing additional structures in the matrix entries, and can be implemented in a wide range of applications with little or no modification. Using probabilistic low-rank factorization, we derive our generalized AMP-based algorithm as an approximation of the loopy belief propagation algorithm. In addition, we apply a rank selection strategy and an expectation-maximization estimation strategy that adaptively obtain the optimal value of the algorithmic parameters. Then, we discuss the specializations of our proposed algorithm to the applications of structured ARM problems, such as compressive hyperspectral imaging and compressive video surveillance. Simulation results with both synthetic and real data demonstrate that the proposed algorithm yields the state-of-the-art reconstruction performance while maintaining competitive computational complexity.
Publikationsart: article
Dateibeschreibung: electronic resource
Sprache: English
ISSN: 2169-3536
Relation: https://ieeexplore.ieee.org/document/7936453/; https://doaj.org/toc/2169-3536
DOI: 10.1109/ACCESS.2017.2710179
Zugangs-URL: https://doaj.org/article/3f0fa652d4444fcf81b59e98d8c0db2c
Dokumentencode: edsdoj.3f0fa652d4444fcf81b59e98d8c0db2c
Datenbank: Directory of Open Access Journals
Beschreibung
Abstract:The topic of the rank minimization problem with affine constraints has been well studied in recent years. However, in many applications the data can exhibit other structures beyond simply being low rank. For example, images and videos present complex spatio-temporal structures, which are largely ignored by current affine rank minimization (ARM) methods. In this paper, we propose a novel approximate message passing (AMP)-based approach that is capable of capturing additional structures in the matrix entries, and can be implemented in a wide range of applications with little or no modification. Using probabilistic low-rank factorization, we derive our generalized AMP-based algorithm as an approximation of the loopy belief propagation algorithm. In addition, we apply a rank selection strategy and an expectation-maximization estimation strategy that adaptively obtain the optimal value of the algorithmic parameters. Then, we discuss the specializations of our proposed algorithm to the applications of structured ARM problems, such as compressive hyperspectral imaging and compressive video surveillance. Simulation results with both synthetic and real data demonstrate that the proposed algorithm yields the state-of-the-art reconstruction performance while maintaining competitive computational complexity.
ISSN:21693536
DOI:10.1109/ACCESS.2017.2710179