Results 11 to 20 of about 156,394 (276)
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
openaire +5 more sources
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
openaire +5 more sources
A Hybrid Norm for Guaranteed Tensor Recovery
Frontiers in Physics, 2022 Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance and
Yihao Luo +5 more
doaj +1 more source
Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization
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 +1 more source
Unconditionality in tensor products and ideals of polynomials, multilinear forms and operators [PDF]
, 2010 We study tensor norms that destroy unconditionality in the following sense: for every Banach space $E$ with unconditional basis, the $n$-fold tensor product of $E$ (with the corresponding tensor norm) does not have unconditional basis.
Carando, Daniel, Galicer, Daniel
core +2 more sources
, 2023 The use of a tensor product perspective has enriched functional analysis and other important areas of mathematics and physics. The context of operator spaces is clearly no exception. The aim of this manuscript is to kick off the development of a systematic theory of tensor products and tensor norms for operator spaces and its interplay with their ...
Chávez-Domínguez, Alejandro +2 more
openaire +2 more sources
Hyperspectral-Multispectral Image Fusion via Tensor Ring and Subspace Decompositions
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Fusion from a spatially low resolution hyperspectral image (LR-HSI) and a spectrally low resolution multispectral image (MSI) to produce a high spatial-spectral HSI (HR-HSI), known as hyperspectral super resolution, has risen to a preferred topic for ...
Honghui Xu +4 more
doaj +1 more source
Arens regularity of projective tensor products [PDF]
, 2015 For completely contractive Banach algebras $A$ and $B$ (respectively operator algebras $A$ and $B$), the necessary and sufficient conditions for the operator space projective tensor product $A\widehat{\otimes}B$ (respectively the Haagerup tensor product $
Kumar, Ajay, Rajpal, Vandana
core +1 more source
Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization
IEEE Access, 2019 Color images can be seen as third-order tensors with column, row and color modes. Considering two inherent characteristics of a color image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar ...
Min Liu, Xinggan Zhang, Lan Tang
doaj +1 more source
Symmetric Tensor Nuclear Norms [PDF]
SIAM Journal on Applied Algebra and Geometry, 2017 25 ...
openaire +3 more sources