Alternating Least-Squares for Low-Rank Matrix Reconstruction

被引：47

作者：

Zachariah, Dave ^{[1
]}

Sundin, Martin ^{[1
]}

Jansson, Magnus ^{[1
]}

Chatterjee, Saikat ^{[1
]}

机构：

[1] KTH Royal Inst Technol, ACCESS Linnaeus Ctr, Stockholm, Sweden

来源：

IEEE SIGNAL PROCESSING LETTERS | 2012年 / 19卷 / 04期

关键词：

Cramer-Rao bound; least squares; low-rank matrix reconstruction; structured matrices; COMPLETION;

D O I：

10.1109/LSP.2012.2188026

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

For reconstruction of low-rank matrices from undersampled measurements, we develop an iterative algorithm based on least-squares estimation. While the algorithm can be used for any low-rank matrix, it is also capable of exploiting a-priori knowledge of matrix structure. In particular, we consider linearly structured matrices, such as Hankel and Toeplitz, as well as positive semidefinite matrices. The performance of the algorithm, referred to as alternating least-squares (ALS), is evaluated by simulations and compared to the Cramer-Rao bounds.

引用

页码：231 / 234

页数：4

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