Results 11 to 20 of about 28,961 (286)
Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completion [PDF]
IEEE Transactions on Image Processing, 2020 The main aim of this paper is to develop a framelet representation of the tensor nuclear norm for third-order tensor completion. In the literature, the tensor nuclear norm can be computed by using tensor singular value decomposition based on the discrete Fourier transform matrix, and tensor completion can be performed by the minimization of the tensor ...
Tai-Xiang Jiang +3 more
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Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 In this paper, we consider the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum. Our model is based on the recently proposed tensor-tensor product (or t-product).
Canyi Lu +5 more
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Nuclear Norm Under Tensor Kronecker Products [PDF]
, 2020 Derksen proved that the spectral norm is multiplicative with respect to vertical tensor products (also known as tensor Kronecker products). We will use this result to show that the nuclear norm and other norms of interest are also multiplicative with respect to vertical tensor products.
Robert Cochrane
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Nuclear norm of higher-order tensors [PDF]
Mathematics of Computation, 2016 23 ...
Shmuel Friedland, Lek‐Heng Lim
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A Joint Fault Diagnosis Scheme Based on Tensor Nuclear Norm Canonical Polyadic Decomposition and Multi-Scale Permutation Entropy for Gears [PDF]
Entropy, 2018 Gears are key components in rotation machinery and its fault vibration signals usually show strong nonlinear and non-stationary characteristics. It is not easy for classical time–frequency domain analysis methods to recognize different gear working ...
Mao Ge +4 more
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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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Low-Rank Tensor Completion for Image and Video Recovery via Capped Nuclear Norm [PDF]
IEEE Access, 2019 Inspired by the robustness and efficiency of the capped nuclear norm, in this paper, we apply it to 3D tensor applications and propose a novel low-rank tensor completion method via tensor singular value decomposition (t-SVD) for image and video recovery.
Xi Chen +5 more
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On Tensor Completion via Nuclear Norm Minimization [PDF]
Foundations of Computational Mathematics, 2015 Many problems can be formulated as recovering a low-rank tensor. Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors. To overcome these difficulties, existing approaches often proceed by unfolding tensors into matrices and then apply ...
Yuan, Ming, Zhang, Cun-Hui
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A QoS Prediction Approach Based on Truncated Nuclear Norm Low-Rank Tensor Completion [PDF]
Sensors, 2022 With the rise of mobile edge computing (MEC), mobile services with the same or similar functions are gradually increasing. Usually, Quality of Service (QoS) has become an indicator to measure high-quality services.
Hong Xia +5 more
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SURE Based Truncated Tensor Nuclear Norm Regularization for Low Rank Tensor Completion [PDF]
2020 28th European Signal Processing Conference (EUSIPCO), 2020 Low rank tensor completion aims to recover the underlying low rank tensor obtained from its partial observations, this has a wide range of applications in Signal Processing and Machine Learning. A number of recent low rank tensor methods have successfully utilised the tensor singular value decomposition method with tensor nuclear norm minimisation via ...
Gordon Morison
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