Results 11 to 20 of about 44,247 (253)
A Fast Algorithm for Convolutional Structured Low-rank Matrix Recovery. [PDF]
IEEE Trans Comput Imaging, 2017 Fourier domain structured low-rank matrix priors are emerging as powerful alternatives to traditional image recovery methods such as total variation and wavelet regularization. These priors specify that a convolutional structured matrix, i.e., Toeplitz, Hankel, or their multi-level generalizations, built from Fourier data of the image should be low ...
Ongie G, Jacob M.
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An Overview of Low-Rank Matrix Recovery From Incomplete Observations [PDF]
IEEE Journal on Selected Topics in Signal Processing, 2016 Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations.
Mark Davenport, Justin Romberg
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A shrinkage principle for heavy-tailed data: High-dimensional robust low-rank matrix recovery [PDF]
Annals of Statistics, 2021 Jianqing Fan, Weichen Wang, Ziwei Zhu
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Sensitivity of low-rank matrix recovery
Numerische Mathematik, 2022 We characterize the first-order sensitivity of approximately recovering a low-rank matrix from linear measurements, a standard problem in compressed sensing. A special case covered by our analysis is approximating an incomplete matrix by a low-rank matrix. We give an algorithm for computing the associated condition number and demonstrate experimentally
Breiding, Paul, Vannieuwenhoven, Nick
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Regularization Parameter Selection for the Low Rank Matrix Recovery [PDF]
Journal of Optimization Theory and Applications, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pan Shang, Lingchen Kong
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Quantization for low-rank matrix recovery [PDF]
Information and Inference: A Journal of the IMA, 2018 Abstract We study Sigma–Delta $(\varSigma\!\varDelta) $ quantization methods coupled with appropriate reconstruction algorithms for digitizing randomly sampled low-rank matrices. We show that the reconstruction error associated with our methods decays polynomially with the oversampling factor, and we leverage our results to obtain root ...
Eric Lybrand, Rayan Saab
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Low-rank Matrix Recovery With Unknown Correspondence
CoRR, 2021 We study a matrix recovery problem with unknown correspondence: given the observation matrix $M_o=[A,\tilde P B]$, where $\tilde P$ is an unknown permutation matrix, we aim to recover the underlying matrix $M=[A,B]$. Such problem commonly arises in many applications where heterogeneous data are utilized and the correspondence among them are unknown, e ...
Zhiwei Tang +3 more
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Maximum Entropy Low-Rank Matrix Recovery [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2018 Fixing ...
Simon Mak, Yao Xie 0002
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Robust low‐rank Hankel matrix recovery for skywave radar slow‐time samples
IET Radar, Sonar & Navigation, 2021 In skywave radar, the slow‐time samples received in a certain range‐azimuth cell are usually processed for signal analysis and target detection. Particularly, to extract the principal components, such as sea clutter and target signal, in slow‐time ...
Baiqiang Zhang, Junhao Xie, Wei Zhou
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Low-rank matrix recovery in poisson noise [PDF]
2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014 This paper describes a new algorithm for recovering low-rank matrices from their linear measurements contaminated with Poisson noise: the Poisson noise Maximum Likelihood Singular Value thresholding (PMLSV) algorithm. We propose a convex optimization formulation with a cost function consisting of the sum of a likelihood function and a regularization ...
Yang Cao 0013, Yao Xie 0002
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