Kernel-based graph learning from smooth signals: A functional viewpoint
The problem of graph learning concerns the construction of an explicit topological structure
revealing the relationship between nodes representing data entities, which plays an …
revealing the relationship between nodes representing data entities, which plays an …
[HTML][HTML] Spectral representation of EEG data using learned graphs with application to motor imagery decoding
Electroencephalography (EEG) data entail a complex spatiotemporal structure that reflects
ongoing organization of brain activity. Characterization of the spatial patterns is an …
ongoing organization of brain activity. Characterization of the spatial patterns is an …
Sparse graph learning under Laplacian-related constraints
JK Tugnait - IEEE Access, 2021 - ieeexplore.ieee.org
We consider the problem of learning a sparse undirected graph underlying a given set of
multivariate data. We focus on graph Laplacian-related constraints on the sparse precision …
multivariate data. We focus on graph Laplacian-related constraints on the sparse precision …
scSGL: kernelized signed graph learning for single-cell gene regulatory network inference
Motivation Elucidating the topology of gene regulatory networks (GRNs) from large single-
cell RNA sequencing datasets, while effectively capturing its inherent cell-cycle …
cell RNA sequencing datasets, while effectively capturing its inherent cell-cycle …
A graph-based approach for missing sensor data imputation
The Internet of Things (IoT) oriented intelligent services require high-quality sensor data
delivery in the wireless sensor networks (WSNs). However, either due to the sensor …
delivery in the wireless sensor networks (WSNs). However, either due to the sensor …
Hypergraph structure inference from data under smoothness prior
Hypergraphs are important for processing data with higher-order relationships involving
more than two entities. In scenarios where explicit hypergraphs are not readily available, it is …
more than two entities. In scenarios where explicit hypergraphs are not readily available, it is …
Representing deep neural networks latent space geometries with graphs
Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art
performance in many machine learning tasks. The core principle of DL methods consists of …
performance in many machine learning tasks. The core principle of DL methods consists of …
Graph-based dynamic modeling and traffic prediction of urban road network
T Liu, A Jiang, X Miao, Y Tang, Y Zhu… - IEEE sensors …, 2021 - ieeexplore.ieee.org
Based on various on-road sensor observations, dynamic modeling and analysis of urban
road networks becomes an important task of an intelligent transportation system. The major …
road networks becomes an important task of an intelligent transportation system. The major …
A unified framework for fair spectral clustering with effective graph learning
X Zhang, Q Wang - arXiv preprint arXiv:2311.13766, 2023 - arxiv.org
We consider the problem of spectral clustering under group fairness constraints, where
samples from each sensitive group are approximately proportionally represented in each …
samples from each sensitive group are approximately proportionally represented in each …
Non parametric graph learning for Bayesian graph neural networks
Graphs are ubiquitous in modelling relationalstructures. Recent endeavours in machine
learningfor graph structured data have led to manyarchitectures and learning algorithms …
learningfor graph structured data have led to manyarchitectures and learning algorithms …