Results 1 to 10 of about 107,644 (266)
Optimal Shrinkage of Singular Values [PDF]
IEEE Transactions on Information Theory, 2017 We consider recovery of low-rank matrices from noisy data by shrinkage of singular values, in which a single, univariate nonlinearity is applied to each of the empirical singular values. We adopt an asymptotic framework, in which the matrix size is much larger than the rank of the signal matrix to be recovered, and the signal-to-noise ratio of the low ...
Matan Gavish, David L. Donoho
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Zhongguo dianliWith no zero-crossing characteristic, DC arc is difficult to extinguish by itself, and is inclined to cause electrical fire, threatening the safety of DC power distribution.
Guoqiang LIU +4 more
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The complex structured singular value [PDF]
Automatica, 1993 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Packard, A., Doyle, J.
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Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform
Applied Sciences, 2023 In recent years, digital image watermarking has gained a significant amount of popularity and developed into a crucial and essential tool for copyright protection, security, and the identification of multimedia content.
Musrrat Ali
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Singular Values of Trilinear Forms [PDF]
Experimental Mathematics, 2001 Let T : H 1 × H 2 × H 3 → C be a trilinear form, where H 1, H 2, H 3 are separable Hilbert spaces. In the hypothesis that at least two of the three spaces are finite dimensional we show that the norm square λ = ∥T∥2 is a root of a certain algebraic equation, usually of very high degree, which we baptize the millennia] equation, because it is an ...
Bo Bernhardsson, Jaak Peetre
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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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Improved singular value inclusion sets for rectangular tensors
Journal of Inequalities and ApplicationsIn this paper, improved singular value inclusion sets for rectangular tensors are given, which are tighter than those in (Zhao and Li in Linear Multilinear Algebra 66(7):1333–1350, 2018).
Jun He, Yanmin Liu
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A generalization of the randomized singular value decomposition
CoRR, 2021 The randomized singular value decomposition (SVD) is a popular and effective algorithm for computing a near-best rank $k$ approximation of a matrix $A$ using matrix-vector products with standard Gaussian vectors. Here, we generalize the randomized SVD to multivariate Gaussian vectors, allowing one to incorporate prior knowledge of $A$ into the ...
Boulle, N, Townsend, A
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On approximating functions of the singular values in a stream [PDF]
Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, 2016 For any real number $p > 0$, we nearly completely characterize the space complexity of estimating $\|A\|_p^p = \sum_{i=1}^n σ_i^p$ for $n \times n$ matrices $A$ in which each row and each column has $O(1)$ non-zero entries and whose entries are presented one at a time in a data stream model. Here the $σ_i$ are the singular values of $A$, and when $p
Yi Li 0002, David P. Woodruff
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Laplacian Singular Values [PDF]
Atlantis Studies in Uncertainty Modelling, 2021 Jirí Janecek, Irina Perfilieva
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