Results 21 to 30 of about 9,171,861 (296)
Low-Rank Matrix Factorization Method for Multiscale Simulations: A Review
IEEE Open Journal of Antennas and Propagation, 2021 In this paper, a review of the low-rank factorization method is presented, with emphasis on their application to multiscale problems. Low-rank matrix factorization methods exploit the rankdeficient nature of coupling impedance matrix blocks between two ...
Mengmeng Li +5 more
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Coupled Low Rank Approximation for Collaborative Filtering in Social Networks
IEEE Access, 2018 Recommending items and friends are equally important in social recommendation applications. Current works usually consider them as two independent tasks and address them separately.
Xianglin Zuo, Xueyan Liu, Bo Yang
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Spatially regularized low-rank tensor approximation for accurate and fast tractography
NeuroImage, 2023 Tractography based on diffusion Magnetic Resonance Imaging (dMRI) is the prevalent approach to the in vivo delineation of white matter tracts in the human brain.
Johannes Gruen +2 more
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From low-rank retractions to dynamical low-rank approximation and back. [PDF]
BIT Numer MathAbstractIn algorithms for solving optimization problems constrained to a smooth manifold, retractions are a well-established tool to ensure that the iterates stay on the manifold. More recently, it has been demonstrated that retractions are a useful concept for other computational tasks on manifold as well, including interpolation tasks.
Séguin A, Ceruti G, Kressner D.
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Generalized Low Rank Approximations of Matrices [PDF]
Machine Learning, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Tensor Robust Principal Component Analysis via Non-Convex Low Rank Approximation
Applied Sciences, 2019 Tensor Robust Principal Component Analysis (TRPCA) plays a critical role in handling high multi-dimensional data sets, aiming to recover the low-rank and sparse components both accurately and efficiently.
Shuting Cai +4 more
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Low-Rank Approximation of Difference between Correlation Matrices Using Inner Product
Applied Sciences, 2021 In the domain of functional magnetic resonance imaging (fMRI) data analysis, given two correlation matrices between regions of interest (ROIs) for the same subject, it is important to reveal relatively large differences to ensure accurate interpretation.
Kensuke Tanioka, Satoru Hiwa
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Modifiable low‐rank approximation to a matrix
Numerical Linear Algebra with Applications, 2009 AbstractA truncated ULV decomposition (TULVD) of an m×n matrix X of rank k is a decomposition of the form X = ULVT+E, where U and V are left orthogonal matrices, L is a k×k non‐singular lower triangular matrix, and E is an error matrix. Only U,V, L, and ∥E∥F are stored, but E is not stored.
Jesse L. Barlow, Hasan Erbay
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Unsupervised Feature Selection via Metric Fusion and Novel Low-Rank Approximation
IEEE Access, 2022 Unsupervised feature selection aims to derive a compact set of features with desired generalization ability via removing the irrelevant and redundant features, yet challenging due to the unavailability of labels.
Yin Long +3 more
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Low Rank Symmetric Tensor Approximations [PDF]
SIAM Journal on Matrix Analysis and Applications, 2017 For a given symmetric tensor, we aim at finding a new one whose symmetric rank is small and that is close to the given one. There exist linear relations among the entries of low rank symmetric tensors. Such linear relations can be expressed by polynomials, which are called generating polynomials.
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