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
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 +4 more
doaj +2 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 +4 more
doaj +2 more sources
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 +2 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 ONETensor-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 +3 more
doaj +2 more sources
An Efficient Tensor Completion Method Via New Latent Nuclear Norm [PDF]
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
doaj +3 more sources
Small Defects Detection of Galvanized Strip Steel via Schatten-p Norm-Based Low-Rank Tensor Decomposition [PDF]
SensorsAccurate 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 +3 more
doaj +2 more sources
Nuclear norm regularized loop optimization for tensor network
Physical Review ResearchWe propose a loop optimization algorithm based on nuclear norm regularization for the tensor network. The key ingredient of this scheme is to introduce a rank penalty term proposed in the context of data processing.
Kenji Homma +2 more
doaj +3 more sources
Norm-Attaining Tensors and Nuclear Operators [PDF]
Mediterranean Journal of Mathematics, 2022 25 pages.
Sheldon Dantas +3 more
openaire +5 more sources
Reshaped tensor nuclear norms for higher order tensor completion [PDF]
Machine Learning, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kishan Wimalawarne, Hiroshi Mamitsuka
openaire +4 more sources