Abstract
We consider the problem of recovering a matrix M that is the sum of a low-rank matrix L and a sparse matrix S from a small set of linear measurements of the form y = A(M) = A(L + S). This model subsumes three important classes of signal recovery problems: compressive sensing, affine rank minimization, and robust principal component analysis. We propose a natural optimization problem for signal recovery under this model and develop a new greedy algorithm called SpaRCS to solve it. Empirically, SpaRCS inherits a number of desirable properties from the state-of-the-art CoSaMP and ADMiRA algorithms, including exponential convergence and efficient implementation. Simulation results with video compressive sensing, hyperspectral imaging, and robust matrix completion data sets demonstrate both the accuracy and efficacy of the algorithm.
Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 24 |
Subtitle of host publication | 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 |
State | Published - Dec 1 2011 |
Event | 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 - Granada, Spain Duration: Dec 12 2011 → Dec 14 2011 |
Other
Other | 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 |
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Country | Spain |
City | Granada |
Period | 12/12/11 → 12/14/11 |
ASJC Scopus subject areas
- Information Systems