Two step recovery of jointly sparse and low-rank matrices: theoretical guarantees
Machine Learning
2015-06-03 v2 Information Theory
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.
Keywords
Cite
@article{arxiv.1412.2669,
title = {Two step recovery of jointly sparse and low-rank matrices: theoretical guarantees},
author = {Sampurna Biswas and Sunrita Poddar and Soura Dasgupta and Raghuraman Mudumbai and Mathews Jacob},
journal= {arXiv preprint arXiv:1412.2669},
year = {2015}
}
Comments
4 pages, 4 figures, ISBI 2015 conference submission