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Two step recovery of jointly sparse and low-rank matrices: Theoretical guarantees
Conference proceeding

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

Sampurna Biswas, Sunrita Poddar, Soura Dasgupta, Raghuraman Mudumbai and Mathews Jacob
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Vol.2015-, pp.914-917
04/2015
DOI: 10.1109/ISBI.2015.7164019
url
https://arxiv.org/pdf/1412.2669View
Open Access

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.
Jacobian matrices Magnetic resonance imaging Dynamic MRI RIP Joint sparsity Sparse matrices Matrix decomposition Joints Low rank Image reconstruction

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