The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited

Hypergraphs allow one 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...

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Bibliographic Details
Published in:arXiv.org 2013-12
Main Authors: Hein, Matthias, Setzer, Simon, Jost, Leonardo, Syama Sundar Rangapuram
Format: Article
Language:English
Subjects:
Approximation
Graph theory
Learning
Regularization
Tensors
Online Access:Get full text
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Summary:Hypergraphs allow one 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.
ISSN:2331-8422