Learning from Multiple Graphs using a Sigmoid Kernel

Abstract : This paper studies the problem of learning from a set of input graphs, each of them representing a different relation over the same set of nodes. Our goal is to merge those input graphs by embedding them into an Euclidean space related to the commute time distance in the original graphs. This is done with the help of a small number of labeled nodes. Our algorithm output a combined kernel that can be used for different graph learning tasks. We consider two combination methods: the (classical) linear combination and the sigmoid combination. We compare the combination methods on node classification tasks using different semi-supervised graph learning algorithms. We note that the sigmoid combination method exhibits very positive results.
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Communication dans un congrès
The 12th International Conference on Machine Learning and Applications (ICMLA'13), Dec 2013, Miami, United States. 2013
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Dernière modification le : jeudi 11 janvier 2018 - 06:25:27
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  • HAL Id : hal-00913237, version 1

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Thomas Ricatte, Gemma Garriga, Rémi Gilleron, Marc Tommasi. Learning from Multiple Graphs using a Sigmoid Kernel. The 12th International Conference on Machine Learning and Applications (ICMLA'13), Dec 2013, Miami, United States. 2013. 〈hal-00913237〉

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