Results 41 to 50 of about 1,952,092 (277)
Nonconvex Low Tubal Rank Tensor Minimization
IEEE Access, 2019 In the sparse vector recovery problem, the L0-norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can be replaced by a convex function or a ...
Yaru Su, Xiaohui Wu, Genggeng Liu
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An Efficient Tensor Completion Method Via New Latent Nuclear Norm
IEEE Access, 2020 In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding schemes.
Jinshi Yu +3 more
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Convex Recovery of Tensors Using Nuclear Norm Penalization [PDF]
, 2015 The subdifferential of convex functions of the singular spectrum of real matrices has been widely studied in matrix analysis, optimization and automatic control theory. Convex analysis and optimization over spaces of tensors is now gaining much interest due to its potential applications to signal processing, statistics and engineering. The goal of this
Stéphane Chrétien, Tianwen Wei
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Low-Rank Tensor Completion via Tensor Nuclear Norm With Hybrid Smooth Regularization
IEEE Access, 2019 As a convex surrogate of tensor multi rank, recently the tensor nuclear norm (TNN) obtains promising results in the tensor completion. However, only considering the low-tubal-rank prior is not enough for recovering the target tensor, especially when the ...
Xi-Le Zhao +4 more
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New estimations on the upper bounds for the nuclear norm of a tensor [PDF]
Journal of Inequalities and Applications, 2018 Using the orthogonal rank of the tensor, a new estimation method for the upper bounds on the nuclear norms is presented and some new tight upper bounds on the nuclear norms are established. Taking into account the structure information of the tensor, an important factor affecting the upper bounds is discussed and some corresponding properties related ...
Xu Kong, Jicheng Li, Xiaolong Wang
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Tensor Rank Estimation and Completion via CP-based Nuclear Norm [PDF]
, 2017 Tensor completion (TC) is a challenging problem of recovering missing entries of a tensor from its partial observation. One main TC approach is based on CP/Tucker decomposition.
Yiu-ming Cheung +5 more
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Robust Tensor Factorization for Color Image and Grayscale Video Recovery
IEEE Access, 2020 Low-rank tensor completion (LRTC) plays an important role in many fields, such as machine learning, computer vision, image processing, and mathematical theory.
Shiqiang Du +4 more
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Hyperspectral Image Denoising via Framelet Transformation Based Three-Modal Tensor Nuclear Norm
Remote Sensing, 2021 During the acquisition process, hyperspectral images (HSIs) are inevitably contaminated by mixed noise, which seriously affects the image quality. To improve the image quality, HSI denoising is a critical preprocessing step.
Wenfeng Kong, Yangyang Song, Jing Liu
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Molecular Oncology, EarlyView.Taxanes are widely used chemotherapeutics whose effects on cellular mechanics remain poorly understood. We show that paclitaxel induces rapid cellular contraction by promoting GEF‐H1 dissociation from microtubules and non‐muscle myosin II activation through RhoA/ROCK.
Gloria Asensio‐Juárez +5 more
wiley +1 more source
Spatiotemporal traffic data imputation via tensorial weighted Schatten‐p norm minimization
IET Intelligent Transport Systems, 2022 Spatiotemporal traffic data exhibit multi‐granular low‐rank structure due to their periodicity among different timelines. Traditional low rank data completion methods fail to characterize such properties and produce unsatisfactory results for data ...
Shaofan Wang +4 more
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