Results 61 to 70 of about 12,418,444 (305)

Low-Rank Matrix Recovery Approach for Clutter Rejection in Real-Time IR-UWB Radar-Based Moving Target Detection

Sensors, 2016
The detection of a moving target using an IR-UWB Radar involves the core task of separating the waves reflected by the static background and by the moving target.
Donatien Sabushimike, Seung You Na, Jin Young Kim, Ngoc Nam Bui, Kyung Sik Seo, Gil Gyeom Kim   +5 more
doaj   +1 more source

Multilayer Sparsity-Based Tensor Decomposition for Low-Rank Tensor Completion

IEEE Transactions on Neural Networks and Learning Systems, 2021
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank (LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes hidden in a tensor, we propose a new multilayer sparsity-based ...
Jize Xue, Yongqiang Zhao, Shaoguang Huang, Wenzhi Liao, J. Chan, S. Kong   +5 more
semanticscholar   +1 more source

Robust low-rank matrix estimation [PDF]

The Annals of Statistics, 2018
Many results have been proved for various nuclear norm penalized estimators of the uniform sampling matrix completion problem. However, most of these estimators are not robust: in most of the cases the quadratic loss function and its modifications are used. We consider robust nuclear norm penalized estimators using two well-known robust loss functions:
Elsener, Andreas, van de Geer, Sara
openaire   +3 more sources

Psychosocial Outcomes in Patients With Endocrine Tumor Syndromes: A Systematic Review

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction The combination of disease manifestations, the familial burden, and varying penetrance of endocrine tumor syndromes (ETSs) is unique. This review aimed to portray and summarize available data on psychosocial outcomes in patients with ETSs and explore gaps and opportunities for future research and care.
Daniël Zwerus, Floortje Strobbe, Annemarie A. Verrijn Stuart, Hanneke M. van Santen, Gerlof D. Valk, Sasja A. Schepers, Rachel S. van Leeuwaarde   +6 more
wiley   +1 more source

Survey on Probabilistic Models of Low-Rank Matrix Factorizations

Entropy, 2017
Low-rank matrix factorizations such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) are a large class of methods for pursuing the low-rank approximation of a given data matrix.
Jiarong Shi, Xiuyun Zheng, Wei Yang
doaj   +1 more source

Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval

Remote Sensing, 2020
As remote sensing (RS) images increase dramatically, the demand for remote sensing image retrieval (RSIR) is growing, and has received more and more attention. The characteristics of RS images, e.g., large volume, diversity and high complexity, make RSIR
Jie Kong, Quansen Sun, Mithun Mukherjee, Jaime Lloret   +3 more
doaj   +1 more source

Nutritional and Behavioral Intervention for Long‐Term Childhood Acute Leukemia Survivors With Metabolic Syndrome

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Purpose Metabolic syndrome (MetS) is a common complication in survivors of childhood acute lymphoblastic and myeloid leukemia (AL), and a major risk factor for premature cardiovascular disease, type‐2‐diabetes, and metabolic dysfunction‐associated steatotic liver disease (MASLD).
Visentin Sandrine, Béliard Sophie, Renard Cécile, Caussy Cyrielle, Hamidou Zeinab, Romano David, Gacem Mélanie, Petit Arnaud, Saultier Paul, Borentain Patrick, Michel Gérard   +10 more
wiley   +1 more source

A novel strategy to upcycle the waste masks: Synthesis of chelating adsorbent and application in heavy metal removal

Desalination and Water Treatment
The COVID-19 pandemic has brought about increasing demand of face masks, however, due to the inadequate disposal, it has caused great environmental problems. In this study, we try to recycle these wastes to fabricate chelating materials for the potential
Dezhi Zhao, Haiping Chen, Yuyang Tian, Yuan Liu   +3 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

Efficient Low Rank Tensor Ring Completion

, 2017
Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS ...
Aeron, Shuchin, Aggarwal, Vaneet, Wang, Wenqi   +2 more
core   +1 more source