A Variational Approach to Stable Principal Component Pursuit
Abstract
We introduce a new convex formulation for stable principal component pursuit (SPCP) to decompose noisy signals into low-rank and sparse representations. For numerical solutions of our SPCP formulation, we first develop a convex variational framework and then accelerate it with quasi-Newton methods. We show, via synthetic and real data experiments, that our approach offers advantages over the classical SPCP formulations in scalability and practical parameter selection.
Cite
Text
Aravkin et al. "A Variational Approach to Stable Principal Component Pursuit." Conference on Uncertainty in Artificial Intelligence, 2014.Markdown
[Aravkin et al. "A Variational Approach to Stable Principal Component Pursuit." Conference on Uncertainty in Artificial Intelligence, 2014.](https://mlanthology.org/uai/2014/aravkin2014uai-variational/)BibTeX
@inproceedings{aravkin2014uai-variational,
title = {{A Variational Approach to Stable Principal Component Pursuit}},
author = {Aravkin, Aleksandr Y. and Becker, Stephen and Cevher, Volkan and Olsen, Peder A.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2014},
pages = {32-41},
url = {https://mlanthology.org/uai/2014/aravkin2014uai-variational/}
}