Matrix ALPS: Accelerated Low Rank and Sparse Matrix Reconstruction

We propose Matrix ALPS for recovering a sparse plus low-rank decomposition of a matrix given its corrupted and incomplete linear measurements. Our approach is a first-order projected gradient method over non-convex sets, and it exploits a well-known memory-based acceleration technique. We theoretically characterize the convergence properties of Matrix ALPS using the stable embedding properties of the linear measurement operator. We then numerically illustrate that our algorithm outperforms the existing convex as well as non-convex state-of-the-art algorithms in computational efficiency without sacrificing stability.


Published in:
2012 Ieee Statistical Signal Processing Workshop (Ssp), 185-188
Presented at:
IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, Michigan, USA, August, 2012
Year:
2012
Publisher:
New York, IEEE
ISBN:
978-1-4673-0183-1
Laboratories:




 Record created 2013-01-14, last modified 2018-09-13

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