SPARSE AND LOW RANK DECOMPOSITION USING l0 PENALTY

被引：0
|
作者：
Ulfarsson, M. O. [1 ]
Solo, V. [2 ]
Marjanovic, G. [2 ]
机构：
[1] Univ Iceland, Dept Elect Engn, Reykjavik, Iceland
[2] Univ New South Wales, Sch Elect Engn, Sydney, NSW, Australia
来源：
2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | 2015年
关键词：
Sparse and Low Rank Matrix Decomposition; Cyclic Descent; Extended BIC; l(0) penalty; MODEL;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
High dimensional data is often modeled as a linear combination of a sparse component, a low-rank component, and noise. An example is a video sequence of a busy scene where the background is the low-rank part and the foreground, e.g. moving pedestrians, is the sparse part. Sparse and low rank (SLR) matrix decomposition is a recent method that estimates those components. In this paper we develop an 10 based SLR method and an associated tuning parameter selection method based on the extended Bayesian information criterion (EBIC) method. In simulations the new algorithm is compared with state of the art algorithms from the literature.
引用
收藏
页码：3312 / 3316
页数：5
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