Results 21 to 30 of about 17,487 (260)
Double Structured Nuclear Norm-Based Matrix Decomposition for Saliency Detection
IEEE Access, 2020 Saliency detection aims at identifying the most important and informative area in a scene. Recently low rank matrix recovery (LR) theory becomes an effective tool for saliency detection.
Junxia Li, Ziyang Wang, Zefeng Pan
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A Log-Det Heuristics for Covariance Matrix Estimation: The Analytic Setup
Stats, 2022 This paper studies a new nonconvex optimization problem aimed at recovering high-dimensional covariance matrices with a low rank plus sparse structure. The objective is composed of a smooth nonconvex loss and a nonsmooth composite penalty.
Enrico Bernardi, Matteo Farnè
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Symmetric Tensor Nuclear Norms [PDF]
SIAM Journal on Applied Algebra and Geometry, 2017 25 ...
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Numerical stability and tensor nuclear norm
Numerische Mathematik, 2023 25 pages, 7 ...
Zhen Dai, Lek-Heng Lim
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On Dropout and Nuclear Norm Regularization
CoRR, 2019 We give a formal and complete characterization of the explicit regularizer induced by dropout in deep linear networks with squared loss. We show that (a) the explicit regularizer is composed of an $\ell_2$-path regularizer and other terms that are also re-scaling invariant, (b) the convex envelope of the induced regularizer is the squared nuclear norm ...
Poorya Mianjy, Raman Arora
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Spectral Norm and Nuclear Norm of a Third Order Tensor
CoRR, 2019 The spectral norm and the nuclear norm of a third order tensor play an important role in the tensor completion and recovery problem. We show that the spectral norm of a third order tensor is equal to the square root of the spectral norm of three positive semi-definite biquadratic tensors, and the square roots of the nuclear norms of those three ...
Liqun Qi 0001, Shenglong Hu
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Spectral norm and nuclear norm of a third order tensor
Journal of Industrial & Management Optimization, 2022 <p style="text-indent:20px;">The spectral norm and the nuclear norm of a third order tensor play an important role in the tensor completion and recovery problem. We show that the spectral norm of a third order tensor is equal to the square root of the spectral norm of three positive semi-definite biquadratic tensors, and the square roots of the ...
Qi, L, Hu, S, Xu, Y
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Mathematics, 2020 Reconstruction of 3D objects in various tomographic measurements is an important problem which can be naturally addressed within the mathematical framework of 3D tensors.
Mohamed Ibrahim Assoweh +2 more
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The Ideal of σ-Nuclear Operators and Its Associated Tensor Norm
Mathematics, 2020 We introduce a new tensor norm ( σ -tensor norm) and show that it is associated with the ideal of σ -nuclear operators. In this paper, we investigate the ideal of σ -nuclear operators and the σ -tensor norm.
Ju Myung Kim, Keun Young Lee
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Joint Local Abundance Sparse Unmixing for Hyperspectral Images
Remote Sensing, 2017 Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity.
Mia Rizkinia, Masahiro Okuda
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