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Two step recovery of jointly sparse and low-rank matrices: theoretical guarantees (1412.2669v2)
Published 5 Dec 2014 in stat.ML, cs.IT, and math.IT
Abstract: 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.