Sparse and low rank decomposition using l0 penalty
Magnus O. Ulfarsson, Victor Solo, Goran Marjanovic
Abstract
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 l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> 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.
BibTeX
@inproceedings{icassp2015_sparseandlowrank,
title = {Sparse and low rank decomposition using l0 penalty},
author = {Magnus O. Ulfarsson and Victor Solo and Goran Marjanovic},
booktitle = {ICASSP 2015},
year = {2015}
}