Results 21 to 30 of about 10,049,409 (261)

Randomized Rank-Revealing QLP for Low-Rank Matrix Decomposition

IEEE Access, 2023
The pivoted QLP decomposition is computed through two consecutive pivoted QR decompositions. It is an approximation to the computationally prohibitive singular value decomposition (SVD). This work is concerned with a partial QLP decomposition of matrices
Maboud F. Kaloorazi, Kai Liu, Jie Chen, Rodrigo C. de Lamare, Susanto Rahardja   +4 more
doaj   +1 more source

A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising

Remote Sensing, 2020
Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods.
Shaobo Li, Jianhu Zhao, Hongmei Zhang, Zijun Bi, Siheng Qu   +4 more
doaj   +1 more source

Low-Rank Methods for Radiation Transport Calculations

, 2023
This dissertation seeks to reduce the computational cost in radiation transport calculations using dynamical low-rank approximation (DLR) methods, a complexity reduction technique to approximate a tensor or a matrix with a reduced rank.
Zhuogang Peng (17675484)
core   +1 more source

Tensor Completion via Smooth Rank Function Low-Rank Approximate Regularization

Remote Sensing, 2023
In recent years, the tensor completion algorithm has played a vital part in the reconstruction of missing elements within high-dimensional remote sensing image data.
Shicheng Yu, Jiaqing Miao, Guibing Li, Weidong Jin, Gaoping Li, Xiaoguang Liu   +5 more
doaj   +1 more source

Dynamical low-rank training of neural networks [PDF]

, 2022
openNeural networks have achieved tremendous success in a large variety of applications. However, their space and time computational demand can limit their usage in resource limited devices. At the same time, overparametrization seems to be necessary in
ZANGRANDO, EMANUELE
core  

Seismic Data Denoising Based on Sparse and Low-Rank Regularization

Energies, 2020
Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications.
Shu Li, Xi Yang, Haonan Liu, Yuwei Cai, Zhenming Peng   +4 more
doaj   +1 more source

Smooth Non-negative Low-Rank Graph Representation for Clustering [PDF]

Jisuanji kexue yu tansuo
The existing low-rank graph representation algorithms fail to capture the global representation structure of data accurately, and cannot make full use of the valid information of data to guide the construction of the representation graph, then the ...
QIAN Luoxiong, CHEN Mei, ZHANG Chi, ZHANG Jinhong, MA Xueyan
doaj   +1 more source

Fractional laplacians viscoelastic wave equation low-rank temporal extrapolation

Frontiers in Earth Science, 2023
The fractional Laplacians constant-Q (FLCQ) viscoelastic wave equation can describe seismic wave propagation accurately in attenuating media. A staggered-grid pseudo-spectral (SGPS) method is usually applied to solve this wave equation but it is of only ...
Hanming Chen, Hanming Chen, Hanming Chen, Lifu Zhang, Lifu Zhang, Lifu Zhang, Hui Zhou, Hui Zhou, Hui Zhou   +8 more
doaj   +1 more source

Nonconvex Low Tubal Rank Tensor Minimization

IEEE Access, 2019
In the sparse vector recovery problem, the L0-norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can be replaced by a convex function or a ...
Yaru Su, Xiaohui Wu, Genggeng Liu
doaj   +1 more source

Robust Generalized Low Rank Approximations of Matrices. [PDF]

PLoS ONE, 2015
In recent years, the intrinsic low rank structure of some datasets has been extensively exploited to reduce dimensionality, remove noise and complete the missing entries.
Jiarong Shi, Wei Yang, Xiuyun Zheng
doaj   +1 more source