Results 11 to 20 of about 138,886 (308)
Fast singular value thresholding without singular value decomposition [PDF]
Methods and Applications of Analysis, 2013 We are interested in solving the following minimization problem Dτ (Y ) := arg min X∈Rm×n 1 2 ∥Y −X∥F + τ∥X∥∗, where Y ∈ Rm×n is a given matrix, and ∥ ⋅ ∥F is the Frobenius norm and ∥ ⋅ ∥∗ the nuclear norm. This problem serves as a basic subroutine in many popular numerical schemes for nuclear norm minimization problems, which arise from low rank ...
Cai, Jianfeng, Stanley, Osher
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Generalized Singular Value Thresholding [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2015 This work studies the Generalized Singular Value Thresholding (GSVT) operator associated with a nonconvex function g defined on the singular values of X. We prove that GSVT can be obtained by performing the proximal operator of g on the singular values since Proxg(.) is monotone when g is lower bounded. If the nonconvex g satisfies some
Canyi Lu +4 more
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svt: Singular Value Thresholding in MATLAB [PDF]
Journal of Statistical Software, 2017 Many statistical learning methods such as matrix completion, matrix regression, and multiple response regression estimate a matrix of parameters. The nuclear norm regularization is frequently employed to achieve shrinkage and low rank solutions.
Cai Li, Hua Zhou
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Singular Value and Structured Singular Value Bounds in Parameter Space [PDF]
, 2002 This contribution discusses the mapping of singular value (sigma-) bounds and structured singuar value (mu-) bounds into parameter space. The sigma- and mu- mapping problem is defined to enclose robut design and analysis in parameter space. Real, complex and mixed uncertainty structures are considered.
Bajcinca, N., Muhler, M.
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Method for Distinguishing the Digital Images in Different Formats [PDF]
Problems of the Regional Energetics, 2022 Nowadays, the energy systems are considered to be the main vital factor for the functioning of society. Today, this part of the infrastructure cannot exist without the informative infrastructure, so it needs efficient information and cyber protection. To
Kobozeva A.A. +2 more
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GPU accelerated singular value thresholding
SoftwareX, 2023 Matrix completion (MC) is widely used in machine learning and signal processing to fill in the missing data of an incomplete observation matrix. Singular value thresholding (SVT) is one of the most popular algorithms among numerous MC methods.
Xiaotong Li, Karel Adámek, Wes Armour
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Prediction of Remaining Useful Life of the Lithium-Ion Battery Based on Improved Particle Filtering
Frontiers in Energy Research, 2022 Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in battery failure prediction and health management (PHM).
Tiezhou Wu +3 more
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Singular Vectors From Singular Values
CoRR, 2020 8 ...
Weiwei Xu, Michael K. Ng 0001
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Adaptive shrinkage of singular values [PDF]
Statistics and Computing, 2015 To recover a low rank structure from a noisy matrix, truncated singular value decomposition has been extensively used and studied. Recent studies suggested that the signal can be better estimated by shrinking the singular values. We pursue this line of research and propose a new estimator offering a continuum of thresholding and shrinking functions. To
Josse, Julie, Sardy, Sylvain
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Singular Value Decomposition of Complexes [PDF]
SIAM Journal on Applied Algebra and Geometry, 2019 Singular value decompositions of matrices are widely used in numerical linear algebra with many applications. In this paper, we extend the notion of singular value decompositions to finite complexes of real vector spaces. We provide two methods to compute them and present several applications.
Danielle A. Brake +4 more
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