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Tensor factorization for multi-relational learning| old_uid | 14920 |
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| title | Tensor factorization for multi-relational learning |
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| start_date | 2015/01/15 |
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| schedule | 14h |
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| online | no |
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| location_info | 25-26, salle 101 |
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| summary | Learning relational data has been of a growing interest in fields as diverse as modeling social networks, semantic web, or bioinformatics. To some extent, a network can be seen as multi-relational data, where a particular relation represents a particular type of link between entities. It can be modeled as a three-way tensor.
Tensor factorization have shown to be a very efficient way to learn such data. It can be done either in a 3-way factorization style (trigram, e.g. RESCAL) or by sum of 2-way factorization (bigram, e.g TransE). Those methods usually achieve state-of-the-art accuracy on benchmarks. Though, all those learning methods suffer from regularization processes which are not always adequate.
We show that both 2-way and 3-way factorization of a relational tensor can be formulated as a simple matrix factorization problem. This class of problems can naturally be relaxed in a convex way. We show that this new method outperforms RESCAL on two benchmarks. |
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| oncancel | séance annulée |
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| responsibles | Piwowarski |
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