HpLapGCN: Hypergraph p-Laplacian graph convolutional networks
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.06.068
Abstract: Abstract Currently, the representation learning of a graph has been proved to be a significant technique to extract graph structured data features. In recent years, many graph representation learning (GRL) algorithms, such as Laplacian Eigenmaps… read more here.
Keywords: hypergraph laplacian; convolutional networks; graph convolutional; graph ... See more keywords
A Tensor Method Based on Enhanced Tensor Nuclear Norm and Hypergraph Laplacian Regularization for Pan-Cancer Omics Data Analysis
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3231908
Abstract: As a powerful data representation technique, tensor robust principal component analysis (TRPCA) has been widely used for clustering and feature selection tasks. However, it ignores the significant difference in singular values of tensor data and… read more here.
Keywords: tensor; enhanced tensor; nuclear norm; tensor nuclear ... See more keywords
Numerical Simulation of Higher-Order Nonlinearity of Human Brain Functional Connectivity Using Hypergraph p-Laplacian
Sign Up to like & getrecommendations! Published in 2021 at "Mathematics"
DOI: 10.3390/math9182345
Abstract: Unravelling how the human brain structure gives rise to function is a central question in neuroscience and remains partially answered. Recent studies show that the graph Laplacian of the human brain’s structural connectivity (SC) plays… read more here.
Keywords: order; connectivity; human brain; hypergraph laplacian ... See more keywords