The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
Machine Learning
2013-12-19 v1 Machine Learning
Optimization and Control
Abstract
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.
Cite
@article{arxiv.1312.5179,
title = {The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited},
author = {Matthias Hein and Simon Setzer and Leonardo Jost and Syama Sundar Rangapuram},
journal= {arXiv preprint arXiv:1312.5179},
year = {2013}
}
Comments
Long version of paper accepted at NIPS 2013