SpaRCS: Recovering low-rank and sparse matrices from compressive measurements

NeurIPS 2011 Andrew E. WatersAswin C. SankaranarayananRichard Baraniuk

We consider the problem of recovering a matrix $\mathbf{M}$ that is the sum of a low-rank matrix $\mathbf{L}$ and a sparse matrix $\mathbf{S}$ from a small set of linear measurements of the form $\mathbf{y} = \mathcal{A}(\mathbf{M}) = \mathcal{A}({\bf L}+{\bf S})$. This model subsumes three important classes of signal recovery problems: compressive sensing, affine rank minimization, and robust principal component analysis... (read more)

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