2013
NIPS
NeurIPS 2013
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
Abstract
Hypergraphs allow to encode higher-order relationships in data and are thus a very flexible modeling tool. Current learning methods are either based on approximations of the hypergraphs via graphs or on tensor methods which are only applicable under special conditions. In this paper we present a new learning framework on hypergraphs which fully uses the hypergraph structure. The key element is a family of regularization functionals based on the total variation on hypergraphs.
🌉
Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— higher-order relationships
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
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Trend Setter
— Graph Neural Networks
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Hot Topic Early Bird
— graph theory
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Optimization & Theory > Optimization
Mathematics & Optimization > Mathematics > Graph Theory
Mathematics & Optimization > Optimization > Continuous Optimization
Machine Learning > Core Methods > Graphical Models
Mathematics & Optimization > Optimization > Optimization
Machine Learning > Learning Types > Representation Learning
Machine Learning > Core Methods > Graph Neural Networks