Results 1 to 10 of about 1,289 (152)

Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization [PDF]

IEEE Access, 2019
In this paper, we study the problem of low-rank tensor completion with the purpose of recovering a low-rank tensor from a tensor with partial observed items. To date, there are several different definitions of tensor ranks.
Yaru Su, Xiaohui Wu, Wenxi Liu
doaj   +4 more sources

Tensor Completion Based on Triple Tubal Nuclear Norm [PDF]

Algorithms, 2018
Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and videos ...
Dongxu Wei, Andong Wang, Xiaoqin Feng, Boyu Wang, Bo Wang   +4 more
doaj   +2 more sources

Bounds on the Spectral Norm and the Nuclear Norm of a Tensor Based on Tensor Partitions [PDF]

SIAM Journal on Matrix Analysis and Applications, 2016
Summary: It is known that computing the spectral norm and the nuclear norm of a tensor is NP-hard in general. In this paper, we provide neat bounds for the spectral norm and the nuclear norm of a tensor based on tensor partitions. The spectral norm (respectively, the nuclear norm) can be lower and upper bounded by manipulating the spectral norms ...
Zhening Li
exaly   +4 more sources

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, Jiashi Feng, Yudong Chen
exaly   +4 more sources

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, Yong Lv, Cancan Yi, Yi Zhang, Xiangjun Chen   +4 more
doaj   +2 more sources

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, Michael K Ng, Xi-Le Zhao   +2 more
exaly   +3 more sources

Interpretable nonconvex submodule clustering algorithm using ℓr-induced tensor nuclear norm and ℓ2,p column sparse norm with global convergence guarantees. [PDF]

PLoS ONE
Tensor-based subspace clustering algorithms have garnered significant attention for their high efficiency in clustering high-dimensional data. However, when dealing with 2D image data, traditional vectorization operations in most algorithms tend to ...
Ming Yang, Shumao Han, Linglong Chen, Jiayi Wang   +3 more
doaj   +2 more sources

On the Synergy between Nonconvex Extensions of the Tensor Nuclear Norm for Tensor Recovery [PDF]

Signals, 2021
Low-rank tensor recovery has attracted much attention among various tensor recovery approaches. A tensor rank has several definitions, unlike the matrix rank—e.g., the CP rank and the Tucker rank. Many low-rank tensor recovery methods are focused on the Tucker rank.
Kaito Hosono, Shunsuke Ono, Takamichi Miyata   +2 more
exaly   +3 more sources

Tensor p-shrinkage nuclear norm for low-rank tensor completion [PDF]

Neurocomputing, 2020
In this paper, a new definition of tensor p-shrinkage nuclear norm (p-TNN) is proposed based on tensor singular value decomposition (t-SVD). In particular, it can be proved that p-TNN is a better approximation of the tensor average rank than the tensor nuclear norm when p < 1.
Chunsheng Liu, Hong Shan, Chunlei Chen   +2 more
exaly   +3 more sources

Small Defects Detection of Galvanized Strip Steel via Schatten-p Norm-Based Low-Rank Tensor Decomposition [PDF]

Sensors
Accurate and efficient white-spot defects detection for the surface of galvanized strip steel is one of the most important guarantees for the quality of steel production.
Shiyang Zhou, Xuguo Yan, Huaiguang Liu, Caiyun Gong   +3 more
doaj   +2 more sources