A variational approach to stable principal component pursuit

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.


Presented at:
30th Conference on Uncertainty in Artificial Intelligence (UAI) 2014, Quebec City, Quebeck, Canada, July 23-27, 2014
Year:
2014
Laboratories:




 Record created 2014-06-12, last modified 2018-01-28

External link:
Download fulltext
n/a
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)