A variational approach to stable principal component pursuit

arXiv1406.1089MaRDI QIDQ6252087

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Publication date: 4 June 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 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.

Has companion code repository: https://github.com/stephenbeckr/fastRPCA

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