Two step recovery of jointly sparse and low-rank matrices: theoretical guarantees

Sampurna Biswas; Sunrita Poddar; Soura Dasgupta; Raghuraman Mudumbai; Mathews Jacob

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arXiv · 1412.2669

Two step recovery of jointly sparse and low-rank matrices: theoretical guarantees

Sampurna Biswas·Sunrita Poddar·Soura Dasgupta·Raghuraman Mudumbai·Mathews Jacob

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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.

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Sampurna Biswas, Sunrita Poddar, Soura Dasgupta, Raghuraman Mudumbai, Mathews Jacob. 2014-12-05. Two step recovery of jointly sparse and low-rank matrices: theoretical guarantees. https://arxiv.org/abs/1412.2669

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