@inproceedings{e59e50e0cb754f0da5c1d0e7e5bcf7fc,
title = "Sparse and low rank decomposition using l0 penalty",
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 recentmethod that estimates those components. In this paper we develop an l0 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.",
keywords = "Cyclic Descent, Extended BIC, Sparse and Low Rank Matrix Decomposition, l penalty",
author = "Ulfarsson, \{M. O.\} and V. Solo and G. Marjanovic",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 ; Conference date: 19-04-2014 Through 24-04-2014",
year = "2015",
month = aug,
day = "4",
doi = "10.1109/ICASSP.2015.7178584",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3312--3316",
booktitle = "2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings",
address = "United States",
}