Generalizing the multiple measurement setting from sparse vector recovery to low-rank matrix recovery (無線通信システム)
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概要
- 論文の詳細を見る
We discuss a novel multiple measurement setting for low-rank matrix recovery in analogy to the approach taken in the sparse vector case. This setting is related to the so-called Generalized Low-Rank Approximation of Matrices (GLRAM) and relies on assuming a joint row and column space model of the matrices. We study the well-posedness of the corresponding optimization problem and propose for its solution an efficient algorithm, based on the classical Alternating Direction Method of multipliers (ADM) algorithm.
- 社団法人電子情報通信学会の論文
- 2011-01-13
著者
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Yamada Isao
Department Of Communications And Integrated Systems Tokyo Institute Of Technology
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GANDY Silvia
Department of Communications and Integrated Systems, Tokyo Institute of Technology
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Gandy Silvia
Department Of Communications And Integrated Systems Tokyo Institute Of Technology
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Yamada Isao
Departmen Of Communications And Integrated Systems Tokyo Institute Of Technology
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