Results 181 to 190 of about 1,665,186 (233)

An efficient recursive identification algorithm for multilinear systems based on tensor decomposition

International Journal of Robust and Nonlinear Control, 2021
There are many important fields involving the multilinear system identification. A great number of parameters to be identified is an important challenge, leading to the need for tensorial decomposition and modeling of such systems.
Yanjiao Wang, Ling-xiang Yang
semanticscholar   +1 more source

Hyperspectral Computational Imaging via Collaborative Tucker3 Tensor Decomposition

IEEE transactions on circuits and systems for video technology (Print), 2021
Computational imaging for hyperspectral images (HSIs) is a hot topic in remote sensing and imaging systems. The dual-camera compressive hyperspectral imaging (DCCHI) system has been successfully designed and applied in hyperspectral imaging. However, the
Yang Xu, Zebin Wu, J. Chanussot, Zhihui Wei   +3 more
semanticscholar   +1 more source

Seismic Data Denoising Based on Tensor Decomposition With Total Variation

IEEE Geoscience and Remote Sensing Letters, 2021
In order to remove random noise in seismic data, this letter proposes a seismic data denoising method based on tensor decomposition and total variation (TDTV).
Jun Feng, Xiaoqin Li, Xi Liu, Chaoxian Chen, Hui Chen   +4 more
semanticscholar   +1 more source

Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network

European Conference on Computer Vision, 2020
Most state of the art deep neural networks are overparameterized and exhibit a high computational cost. A straightforward approach to this problem is to replace convolutional kernels with its low-rank tensor approximations, whereas the Canonical Polyadic
A. Phan, Konstantin Sobolev, Konstantin Sozykin, Dmitry Ermilov, Julia Gusak, P. Tichavský, V. Glukhov, I. Oseledets, A. Cichocki   +8 more
semanticscholar   +1 more source

Deep Convolutional Neural Network Compression via Coupled Tensor Decomposition

IEEE Journal on Selected Topics in Signal Processing, 2021
Large neural networks have aroused impressive progress in various real world applications. However, the expensive storage and computational resources requirement for running deep networks make them problematic to be deployed on mobile devices.
Weize Sun, Shaowu Chen, Lei Huang, H. So, Min Xie   +4 more
semanticscholar   +1 more source

Tensor Decomposition Via Core Tensor Networks

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
Tensor decomposition (TD) has shown promising performance in image completion and denoising. Existing methods always aim to decompose one tensor into latent factors or core tensors by optimizing a particular cost function based on a specific tensor model.
Jianfu ZHANG, ZERUI TAO, LIQING ZHANG, QIBIN ZHAO   +3 more
openaire   +1 more source

A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

IEEE/CAA Journal of Automatica Sinica
Unmanned aerial vehicles (UAVs) have gained significant attention in practical applications, especially the low-altitude aerial (LAA) object detection imposes stringent requirements on recognition accuracy and computational resources.
Nianyin Zeng, Xinyu Li, Peishu Wu, Han Li, Xin Luo   +4 more
semanticscholar   +1 more source

Coarray Tensor Train Decomposition for Bistatic MIMO Radar With Uniform Planar Array

IEEE Transactions on Antennas and Propagation
Recently, tensor network decomposition has attracted increasing attention due to its high efficacy in modeling multidimensional correlations in high-order tensors. Considering this advantage, this study employs tensor train decomposition (TTD) to achieve
Qianpeng Xie, Zhangling Wang, Fangqing Wen, Jin He, Trieu-Kien Truong   +4 more
semanticscholar   +1 more source

Hyperspectral Image Denoising With Total Variation Regularization and Nonlocal Low-Rank Tensor Decomposition

IEEE Transactions on Geoscience and Remote Sensing, 2020
Hyperspectral images (HSIs) are normally corrupted by a mixture of various noise types, which degrades the quality of the acquired image and limits the subsequent application.
Hongyan Zhang, Lu Liu, Wei He, Liangpei Zhang   +3 more
semanticscholar   +1 more source

Privacy-Preserving Tensor Decomposition Over Encrypted Data in a Federated Cloud Environment

IEEE Transactions on Dependable and Secure Computing, 2020
Tensors are popular and versatile tools which model multidimensional data. Tensor decomposition has emerged as a powerful technique dealing with multidimensional data.
Jun Feng, L. Yang, Qing Zhu, Kim-Kwang Raymond Choo   +3 more
semanticscholar   +1 more source