Iterative Hard Thresholding for Low-Rank Recovery from Rank-One Projections

Simon Foucart; Srinivas Subramanian

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arXiv · 1810.11749

Iterative Hard Thresholding for Low-Rank Recovery from Rank-One Projections

Simon Foucart·Srinivas Subramanian

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Abstract

A novel algorithm for the recovery of low-rank matrices acquired via compressive linear measurements is proposed and analyzed. The algorithm, a variation on the iterative hard thresholding algorithm for low-rank recovery, is designed to succeed in situations where the standard rank-restricted isometry property fails, e.g. in case of subexponential unstructured measurements or of subgaussian rank-one measurements. The stability and robustness of the algorithm are established based on distinctive matrix-analytic ingredients and its performance is substantiated numerically.

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Simon Foucart, Srinivas Subramanian. 2018-10-28. Iterative Hard Thresholding for Low-Rank Recovery from Rank-One Projections. https://arxiv.org/abs/1810.11749

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