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Two step recovery of jointly sparse and low-rank matrices: theoretical guarantees
Sampurna Biswas, Sunrita Poddar, Soura Dasgupta, Raghuraman Mudu · 2014-12-06 · via cs.IT updates on arXiv.org

We introduce a two step algorithm with theoretical guarantees to recover a jointly sparse and low-rank matrix from undersampled measurements of its columns. The algorithm first estimates the row subspace of the matrix using a set of common measurements of the columns. In the second step, the subspace aware recovery of the matrix is solved using a simple least square algorithm. The results are verified in the context of recovering CINE data from undersampled measurements; we obtain good recovery when the sampling conditions are satisfied.

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