Results 81 to 90 of about 1,665,186 (233)

Tensor-Based Low-Rank and Sparse Prior Information Constraints for Hyperspectral Image Denoising

IEEE Access, 2020
Hyperspectral data have been widely used in various fields due to its rich spectral and spatial information in recent years. Yet, hyperspectral images are always tainted by a variety of mixed noises.
Guxi Wang, Hongwei Han, Emmanuel John M. Carranza, Si Guo, Ke Guo, Keyan Xiao   +5 more
doaj   +1 more source

Rank revealing‐based tensor completion using improved generalized tensor multi‐rank minimization

IET Signal Processing, 2021
The authors address the problem of tensor completion from limited samplings. An improved generalized tubal Kronecker decomposition is first proposed to reveal the tensor structure of the targeted data, and the improved generalized tensor tubal‐rank and ...
Wei Z. Sun, Peng Zhang, Bo Zhao
doaj   +1 more source

Tensor Train-Based Higher-Order Dynamic Mode Decomposition for Dynamical Systems

Mathematics, 2023
Higher-order dynamic mode decomposition (HODMD) has proved to be an efficient tool for the analysis and prediction of complex dynamical systems described by data-driven models.
Keren Li, Sergey Utyuzhnikov
doaj   +1 more source

Provable Sparse Tensor Decomposition

, 2016
We propose a novel sparse tensor decomposition method, namely Tensor Truncated Power (TTP) method, that incorporates variable selection into the estimation of decomposition components. The sparsity is achieved via an efficient truncation step embedded in
Cheng, Guang, Liu, Han, Lu, Junwei, Sun, Will Wei   +3 more
core   +1 more source

Fast and Guaranteed Tensor Decomposition via Sketching [PDF]

, 2015
Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in statistical learning of latent variable models and in data mining. In this paper, we propose fast and randomized tensor CP decomposition algorithms based on sketching.
Anandkumar, Animashree, Smola, Alexander, Tung, Hsiao-Yu, Wang, Yining   +3 more
core   +4 more sources

Guaranteed Functional Tensor Singular Value Decomposition

Journal of the American Statistical Association, 2023
Journal of the American Statistical Association, to ...
Han, Rungang, Shi, Pixu, Zhang, Anru R.
openaire   +3 more sources

Constrained Cramér-Rao Bound for Higher-Order Singular Value Decomposition

IEEE Open Journal of Signal Processing
Tensor decomposition methods for signal processing applications are an active area of research. Real data are often low-rank, noisy, and come in a higher-order format.
Metin Calis, Massimo Mischi, Alle-Jan van der Veen, Raj Thilak Rajan, Borbala Hunyadi   +4 more
doaj   +1 more source

High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition [PDF]

Journal of the American Statistical Association, 2019
The classical vector autoregressive model is a fundamental tool for multivariate time series analysis. However, it involves too many parameters when the number of time series and lag order are even moderately large. This article proposes to rearrange the
Di Wang, H. Lian, Yao Zheng, Guodong Li
semanticscholar   +1 more source

Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction

Photonics
Energy spectrum computed tomography (CT) technology based on photon-counting detectors has been widely used in many applications such as lesion detection, material decomposition, and so on.
Xuru Li, Kun Wang, Yan Chang, Yaqin Wu, Jing Liu   +4 more
doaj   +1 more source

Decomposition of tensors

, 2014
We consider representations of tensors as sums of decomposable tensors or, equivalently, decomposition of multilinear forms into one--forms. In this short note we show that there exists a particular finite strongly orthogonal decomposition which is essentially unique and yields all critical points of the multilinear form on the torus.
Peña, Juan Manuel, Sauer, Tomas
openaire   +2 more sources