HRST-LR: a hessian regularization spatio-temporal low rank algorithm for traffic data imputation
Intelligent Transportation Systems (ITSs) are vital for alleviating traffic congestion and
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …
Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images
Abstract Coronavirus Disease 2019 (COVID-19) still presents a pandemic trend globally.
Detecting infected individuals and analyzing their status can provide patients with proper …
Detecting infected individuals and analyzing their status can provide patients with proper …
A nonlocal similarity learning-based tensor completion model with its application in intelligent transportation system
Predicting the traffic flow has been one of the most important applications in intelligent
transportation system. However, the missing information in the traffic data will directly affect …
transportation system. However, the missing information in the traffic data will directly affect …
Hyperspectral anomaly detection based on tensor ring decomposition with factors TV regularization
M Feng, W Chen, Y Yang, Q Shu, H Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Anomaly detection in the hyperspectral image (HSI) has gradually become a hot topic in
remote sensing. Recently, some tensor-based methods have been proposed to improve …
remote sensing. Recently, some tensor-based methods have been proposed to improve …
Quaternion-based color image completion via logarithmic approximation
L Yang, J Miao, KI Kou - Information Sciences, 2022 - Elsevier
In color image processing, the objective of image completion is to restore missing entries
from the incomplete observation image. Recent improvements have assisted in resolving the …
from the incomplete observation image. Recent improvements have assisted in resolving the …
Robust low tubal rank tensor completion via factor tensor norm minimization
W Jiang, J Zhang, C Zhang, L Wang, H Qi - Pattern Recognition, 2023 - Elsevier
Recent research has demonstrated that low tubal rank recovery based on tensor has
received extensive attention. In this correspondence, we define tensor double nuclear norm …
received extensive attention. In this correspondence, we define tensor double nuclear norm …
Truncated quadratic norm minimization for bilinear factorization based matrix completion
Low-rank matrix completion is an important research topic with a wide range of applications.
One prevailing way for matrix recovery is based on rank minimization. Directly solving this …
One prevailing way for matrix recovery is based on rank minimization. Directly solving this …
Accelerated PALM for nonconvex low-rank matrix recovery with theoretical analysis
Low-rank matrix recovery is a major challenge in machine learning and computer vision,
particularly for large-scale data matrices, as popular methods involving nuclear norm and …
particularly for large-scale data matrices, as popular methods involving nuclear norm and …
Robust tensor completion via capped Frobenius norm
Tensor completion (TC) refers to restoring the missing entries in a given tensor by making
use of the low-rank structure. Most existing algorithms have excellent performance in …
use of the low-rank structure. Most existing algorithms have excellent performance in …
Attention-guided low-rank tensor completion
Low-rank tensor completion (LRTC) aims to recover missing data of high-dimensional
structures from a limited set of observed entries. Despite recent significant successes, the …
structures from a limited set of observed entries. Despite recent significant successes, the …