Results 121 to 130 of about 2,491 (218)

Higher-Order Regularization Learning on Hypergraphs

CoRR
Higher-Order Hypergraph Learning (HOHL) was recently introduced as a principled alternative to classical hypergraph regularization, enforcing higher-order smoothness via powers of multiscale Laplacians induced by the hypergraph structure. Prior work established the well- and ill-posedness of HOHL through an asymptotic consistency analysis in geometric ...
Adrien Weihs, Andrea L. Bertozzi, Matthew Thorpe   +2 more
openaire   +2 more sources

Hypergraph regularized nonnegative triple decomposition for multiway data analysis

Scientific Reports
Tucker decomposition is widely used for image representation, data reconstruction, and machine learning tasks, but the calculation cost for updating the Tucker core is high.
Qingshui Liao, Qilong Liu, Fatimah Abdul Razak   +2 more
doaj   +1 more source

Cross-view contrastive learning and multi-channel hypergraph convolution for session-based recommendation

Dianxin kexue
Session-based recommendation predicts the next interaction item by analyzing anonymous users’ historical interaction data. Because of the sparsity of user behavior data, modeling session representations from a single perspective may fail to fully capture
REN Yubin, WANG Ruiqin, SUI Xinyi, FANG Chi   +3 more
doaj  

Hypergraph-based inductive learning for generating implicit key phrases

, 2011
This paper presents a novel approach to generate implicit key phrases which are ignored in previous researches. Recent researches prefer to extract key phrases with semi-supervised transductive learning methods, which avoid the problem of training data ...
Li, Sujian, Decong Li, Li, Decong, Sujian Li   +3 more
core   +1 more source

HyperG-PS: Voxel correlation modeling via hypergraph for LiDAR panoptic segmentation

Fundamental Research
Light-detection-and-ranging (LiDAR) point cloud panoptic segmentation is a fundamental task in autonomous driving since it integrates the tasks of static environmental understanding and dynamic object identification, which have recently gained ...
Lin Bie, Gang Xiao, Yipeng Li, Yue Gao
doaj   +1 more source

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks. [PDF]

PLoS Comput Biol, 2023
Jin S, Hong Y, Zeng L, Jiang Y, Lin Y, Wei L, Yu Z, Zeng X, Liu X.   +8 more
europepmc   +1 more source

Hypergraph p-Laplacian: A Differential Geometry View

, 2018
The graph Laplacian plays key roles in information processing of relational data, and has analogies with the Laplacian in differential geometry. In this paper, we generalize the analogy between graph Laplacian and differential geometry to the hypergraph ...
Mandic, Danilo, Saito, Shota, Suzuki, Hideyuki   +2 more
core   +1 more source

Causal-aware spatio-temporal hypergraph learning for early health risk prediction in community elderly care

Alexandria Engineering Journal
As the global population ages, community-based healthcare systems increasingly rely on early identification of health risks to prevent acute events and maintain quality of life for older adults.
Zheng Fang
doaj   +1 more source

Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction. [PDF]

Adv Sci (Weinh), 2023
Jiang Y, Wang R, Feng J, Jin J, Liang S, Li Z, Yu Y, Ma A, Su R, Zou Q, Ma Q, Wei L.   +11 more
europepmc   +1 more source

Analysis of Semi-Supervised Learning on Hypergraphs

CoRR
Hypergraphs provide a natural framework for modeling higher-order interactions, yet their theoretical underpinnings in semi-supervised learning remain limited. We provide an asymptotic consistency analysis of variational learning on random geometric hypergraphs, precisely characterizing the conditions ensuring the well-posedness of hypergraph learning ...
Adrien Weihs, Andrea L. Bertozzi, Matthew Thorpe   +2 more
openaire   +2 more sources