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   Articles with "rank approximation" as a keyword



Image denoising via structure-constrained low-rank approximation

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recommendations! Published in 2020 at "Neural Computing and Applications"

DOI: 10.1007/s00521-020-04717-w

Abstract: Low-rank approximation-based methods have recently achieved impressive results in image restoration. Generally, the low-rank constraint integrated with the nonlocal self-similarity prior is enforced for image recovery. However, it is still unsatisfactory to recover complex image… read more here.

Keywords: image; structure constrained; rank approximation; low rank ... See more keywords

Discontinuous Galerkin discretization of conservative dynamical low-rank approximation schemes for the Vlasov–Poisson equation

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recommendations! Published in 2025 at "BIT Numerical Mathematics"

DOI: 10.1007/s10543-025-01085-6

Abstract: A numerical dynamical low-rank approximation (DLRA) scheme for the solution of the Vlasov–Poisson equation is presented. Based on the formulation of the DLRA equations as Friedrichs’ systems in a continuous setting, it combines recently proposed… read more here.

Keywords: rank; poisson equation; rank approximation; dynamical low ... See more keywords

A Robust Numerical Method for the Random Interface Grating Problem via Shape Calculus, Weak Galerkin Method, and Low-Rank Approximation

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recommendations! Published in 2018 at "Journal of Scientific Computing"

DOI: 10.1007/s10915-018-0712-z

Abstract: We present an efficient numerical algorithm to solve random interface grating problems based on a combination of shape derivatives, the weak Galerkin method, and a low-rank approximation technique. By using the asymptotic perturbation approach via… read more here.

Keywords: method; weak galerkin; rank approximation; low rank ... See more keywords

Rank-1 Approximation for Entangled Multipartite Real Systems

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recommendations! Published in 2022 at "Journal of Scientific Computing"

DOI: 10.1007/s10915-022-01805-y

Abstract: The interaction of multiple parts with each other within a system according to certain intrinsic rules is a crucial natural phenomenon. The notion of entanglement and its decomposition of high-dimensional arrays is particularly intriguing since… read more here.

Keywords: paper; entangled multipartite; problem; approximation ... See more keywords
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Implementation of the unscented transformation with low rank approximation in uncertainty analysis during large-break loss of coolant accident

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recommendations! Published in 2020 at "Annals of Nuclear Energy"

DOI: 10.1016/j.anucene.2020.107614

Abstract: Abstract The Low Rank Approximation (LRA) and Unscented Transform (UT) are integrated to produce a new algorithm having the capability to decrease the time required for the uncertainty quantification during Loss of coolant accident (LOCA)… read more here.

Keywords: coolant accident; sigma points; rank approximation; low rank ... See more keywords

Exact solutions in low-rank approximation with zeros

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recommendations! Published in 2022 at "Linear Algebra and its Applications"

DOI: 10.1016/j.laa.2022.01.021

Abstract: Low-rank approximation with zeros aims to find a matrix of fixed rank and with a fixed zero pattern that minimizes the Euclidean distance to a given data matrix. We study the critical points of this… read more here.

Keywords: rank; rank approximation; approximation zeros; low rank ... See more keywords

A hybrid algorithm for low-rank approximation of nonnegative matrix factorization

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recommendations! Published in 2019 at "Neurocomputing"

DOI: 10.1016/j.neucom.2019.07.059

Abstract: Abstract Nonnegative matrix factorization (NMF) is a recently developed method for data analysis. So far, most of known algorithms for NMF are based on alternating nonnegative least squares (ANLS) minimization of the squared Euclidean distance… read more here.

Keywords: low rank; rank approximation; algorithm; nonnegative matrix ... See more keywords

Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification

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recommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"

DOI: 10.1109/jstars.2022.3199885

Abstract: Given the detrimental effect of spectral variations in a hyperspectral image (HSI), this article investigates to recover its discriminative representation to improve the classification performance. We propose a new method, namely local low-rank approximation with… read more here.

Keywords: rank approximation; low rank; llra slpg; slpg ... See more keywords

Super-Resolution Hyperspectral Reconstruction With Majorization-Minimization Algorithm and Low-Rank Approximation

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recommendations! Published in 2022 at "IEEE Transactions on Computational Imaging"

DOI: 10.1109/tci.2022.3161849

Abstract: Hyperspectral imaging (HSI) has become an invaluable imaging tool for many applications in astrophysics or Earth observation. Unfortunately, direct observation of hyperspectral images is impossible since the actual measurements are 2-D and suffer from strong… read more here.

Keywords: rank approximation; resolution hyperspectral; low rank; resolution ... See more keywords

Hyperspectral Image Denoising Using Factor Group Sparsity-Regularized Nonconvex Low-Rank Approximation

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recommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2021.3110769

Abstract: Hyperspectral image (HSI) mixed noise removal is a fundamental problem and an important preprocessing step in remote sensing fields. The low-rank approximation-based methods have been verified effective to encode the global spectral correlation for HSI… read more here.

Keywords: factor group; hsi denoising; rank approximation; low rank ... See more keywords

Tensor Low-Rank Approximation via Plug-and-Play Priors for Anomaly Detection in Remote Sensing Images

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recommendations! Published in 2025 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2025.3553235

Abstract: Optical remote sensing images (RSIs) have received widespread attention in fields such as agricultural monitoring, mineral exploration, and military defense. However, the detection performance will be seriously degraded when interfered with by noise. To overcome… read more here.

Keywords: rank approximation; remote sensing; tensor low; sensing images ... See more keywords