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GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery [PDF]
SIAM Journal on Mathematics of Data Science, 2022 Low rank matrix recovery problems, including matrix completion and matrix sensing, appear in a broad range of applications. In this work we present GNMR -- an extremely simple iterative algorithm for low rank matrix recovery, based on a Gauss-Newton linearization.
Boaz Nadler
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A Low-Rank Matrix Recovery Approach for Energy Efficient EEG Acquisition for a Wireless Body Area Network [PDF]
Sensors, 2014 We address the problem of acquiring and transmitting EEG signals in Wireless Body Area Networks (WBAN) in an energy efficient fashion. In WBANs, the energy is consumed by three operations: sensing (sampling), processing and transmission. Previous studies
Angshul Majumdar +2 more
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Proximal iteratively reweighted algorithm for low-rank matrix recovery [PDF]
Journal of Inequalities and Applications, 2018 This paper proposes a proximal iteratively reweighted algorithm to recover a low-rank matrix based on the weighted fixed point method. The weighted singular value thresholding problem gains a closed form solution because of the special properties of ...
Chao-Qun Ma, Yi-Shuai Ren
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Low-Rank Matrix Recovery from Noise via an MDL Framework-Based Atomic Norm [PDF]
Sensors, 2020 The recovery of the underlying low-rank structure of clean data corrupted with sparse noise/outliers is attracting increasing interest. However, in many low-level vision problems, the exact target rank of the underlying structure and the particular ...
Anyong Qin +4 more
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Estimation of Overspread Underwater Acoustic Channel Based on Low-Rank Matrix Recovery [PDF]
Sensors, 2019 In this paper, the estimation of overspread, i.e., doubly spread underwater acoustic (UWA) channels of strong dispersion is considered. We show that although the UWA channel dispersion causes the degeneration of channel sparsity, it leads to a low-rank ...
Jie Li +4 more
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Improved low-rank matrix recovery method for predicting miRNA-disease association [PDF]
Scientific Reports, 2017 MicroRNAs (miRNAs) performs crucial roles in various human diseases, but miRNA-related pathogenic mechanisms remain incompletely understood. Revealing the potential relationship between miRNAs and diseases is a critical problem in biomedical research ...
Li Peng +5 more
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Nonconvex Robust Low-Rank Matrix Recovery [PDF]
SIAM Journal on Optimization, 2020 In this paper we study the problem of recovering a low-rank matrix from a number of random linear measurements that are corrupted by outliers taking arbitrary values. We consider a nonsmooth nonconvex formulation of the problem, in which we explicitly enforce the low-rank property of the solution by using a factored representation of the matrix ...
Zhihui Zhu +2 more
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Matrix recovery with implicitly low-rank data [PDF]
Neurocomputing, 2019 In this paper, we study the problem of matrix recovery, which aims to restore a target matrix of authentic samples from grossly corrupted observations. Most of the existing methods, such as the well-known Robust Principal Component Analysis (RPCA), assume that the target matrix we wish to recover is low-rank.
Jun Wang, Xingyu Xie, Guangcan Liu
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Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
Applied Sciences, 2023 An approach to parameter optimization for the low-rank matrix recovery method in hyperspectral imaging is discussed. We formulate an optimization problem with respect to the initial parameters of the low-rank matrix recovery method.
Monika Wolfmayr
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Guarantees of Riemannian Optimization for Low Rank Matrix Recovery [PDF]
SIAM Journal on Matrix Analysis and Applications, 2016 We establish theoretical recovery guarantees of a family of Riemannian optimization algorithms for low rank matrix recovery, which is about recovering an $m\times n$ rank $r$ matrix from $p < mn$ number of linear measurements. The algorithms are first interpreted as iterative hard thresholding algorithms with subspace projections.
Leung Shing Yu, Ke Wei, Cai Jian-Feng
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