A variational approach to stable principal component pursuit

Aleksandr Aravkin, Stephen Becker, Volkan Cevher, Peder Olsen
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:558-567, 2014.

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 solu- tions of our SPCP formulation, we first de- velop a convex variational framework and then accelerate it with quasi-Newton meth- ods. We show, via synthetic and real data experiments, that our approach offers advan- tages over the classical SPCP formulations in scalability and practical parameter selection.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-aravkin14a, title = {A variational approach to stable principal component pursuit}, author = {Aravkin, Aleksandr and Becker, Stephen and Cevher, Volkan and Olsen, Peder}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {558--567}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/aravkin14a/aravkin14a.pdf}, url = {https://proceedings.mlr.press/r12/aravkin14a.html}, 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 solu- tions of our SPCP formulation, we first de- velop a convex variational framework and then accelerate it with quasi-Newton meth- ods. We show, via synthetic and real data experiments, that our approach offers advan- tages over the classical SPCP formulations in scalability and practical parameter selection.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A variational approach to stable principal component pursuit %A Aleksandr Aravkin %A Stephen Becker %A Volkan Cevher %A Peder Olsen %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-aravkin14a %I PMLR %P 558--567 %U https://proceedings.mlr.press/r12/aravkin14a.html %V R12 %X We introduce a new convex formulation for stable principal component pursuit (SPCP) to decompose noisy signals into low-rank and sparse representations. For numerical solu- tions of our SPCP formulation, we first de- velop a convex variational framework and then accelerate it with quasi-Newton meth- ods. We show, via synthetic and real data experiments, that our approach offers advan- tages over the classical SPCP formulations in scalability and practical parameter selection. %Z Reissued by PMLR on 04 October 2026.
APA
Aravkin, A., Becker, S., Cevher, V. & Olsen, P.. (2014). A variational approach to stable principal component pursuit. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:558-567 Available from https://proceedings.mlr.press/r12/aravkin14a.html. Reissued by PMLR on 04 October 2026.

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