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Shared and Individual Resting-State MEG Network Signatures of Tinnitus Revealed by Holistic Graph Learning [PDF]

IEEE Open Journal of Engineering in Medicine and Biology
Tinnitus, the perception of sound without an external source, affects many individuals, yet its impact on the brain’s functional connectome remains underexplored. Traditional functional connectivity (FC) methods, such as Pearson correlation, phase
Payam S. Shabestari, Harry H. Behjat, Dimitri Van De Ville, Christopher R. Cederroth, Niklas K. Edvall, Adrian Naas, Tobias Kleinjung, Patrick Neff   +7 more
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Graph learning based suicidal ideation detection via tree-drawing test [PDF]

Frontiers in Psychiatry
IntroductionAdolescent suicide is a critical public health concern worldwide, necessitating effective methods for early detection of high suicidal ideation.
Ye Liu, Jiashuo Zheng, Yang Zeng, Fang Luo, Xuetao Tian, Xuetao Tian   +5 more
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Network representation learning based on social similarities

Frontiers in Environmental Science, 2022
Analysis of large-scale networks generally requires mapping high-dimensional network data to a low-dimensional space. We thus need to represent the node and connections accurate and effectively, and representation learning could be a promising method. In
Ziwei Mo, Zhenzhen Xie, Xilian Zhang, Qi Luo, Yanwei Zheng, Dongxiao Yu   +5 more
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OGSSL: A Semi-Supervised Classification Model Coupled With Optimal Graph Learning for EEG Emotion Recognition

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022
Electroencephalogram(EEG) signals are generated from central nervous system which are difficult to disguise, leading to its popularity in emotion recognition. Recently,semi-supervisedlearning exhibits promisingemotion recognition performance by involving
Yong Peng, Fengzhe Jin, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, Andrzej Cichocki   +5 more
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Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment

Technologies, 2023
Cognitive decline represents a significant public health concern due to its severe implications on memory and general health. Early detection is crucial to initiate timely interventions and improve patient outcomes. However, traditional diagnosis methods
Gabriel Antonesi, Alexandru Rancea, Tudor Cioara, Ionut Anghel   +3 more
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Prediction of Synergistic Antibiotic Combinations by Graph Learning

Frontiers in Pharmacology, 2022
Antibiotic resistance is a major public health concern. Antibiotic combinations, offering better efficacy at lower doses, are a useful way to handle this problem.
Ji Lv, Ji Lv, Guixia Liu, Guixia Liu, Yuan Ju, Ying Sun, Weiying Guo   +6 more
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Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding

Remote Sensing, 2021
Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative ...
Guangyao Shi, Fulin Luo, Yiming Tang, Yuan Li   +3 more
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Adaptive Graph Representation for Clustering

IEEE Access, 2022
Many graph construction methods for clustering cannot consider both local and global data structures in the construction of initial graph. Meanwhile, redundant features or even outliers and data with important characteristics are addressed equally in the
Mei Chen, Youshuai Wang, Yongxu Chen, Hongyu Zhu, Yue Xie, Pengju Guo   +5 more
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Multiple Superpixel Graphs Learning Based on Adaptive Multiscale Segmentation for Hyperspectral Image Classification

Remote Sensing, 2022
Hyperspectral image classification (HSIC) methods usually require more training samples for better classification performance. However, a large number of labeled samples are difficult to obtain because it is cost- and time-consuming to label an HSI in a ...
Chunhui Zhao, Boao Qin, Shou Feng, Wenxiang Zhu   +3 more
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Self-supervised Dynamic Graph Representation Learning Approach Based on Contrastive Prediction [PDF]

Jisuanji kexue, 2023
In recent years,graph self-supervised learning represented by graph contrastive learning has become a hot research to-pic in the field of graph learning.This learning paradigm does not depend on node labels and has good generalization ability.However ...
JIANG Linpu, CHEN Kejia
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