期刊
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
卷 10, 期 4, 页码 608-622出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTSP.2016.2539100
关键词
Blind deconvolution; low-rank matrices; matrix completion; matrix recovery algorithms; phase retrieval
资金
- NRL [N00173-14-2-C001]
- AFOSR [FA9550-14-1-0342]
- NSF [CCF-1409406, CCF-1350616, CMMI-1537261, CCF-1422540]
- ONR [N00014-11-1-0459]
- Packard Foundation
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1350616] Funding Source: National Science Foundation
Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. This paper provides an overview of modern techniques for exploiting low-rank structure to perform matrix recovery in these settings, providing a survey of recent advances in this rapidly-developing field. Specific attention is paid to the algorithms most commonly used in practice, the existing theoretical guarantees for these algorithms, and representative practical applications of these techniques.
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