Logistic Tensor Factorization for Multi-Relational Data

Maximilian Nickel; Volker Tresp

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arxiv 1306.2084 v1 pith:H7QYYMIG submitted 2013-06-10 stat.ML cs.LG

Logistic Tensor Factorization for Multi-Relational Data

Maximilian Nickel , Volker Tresp This is my paper

classification stat.ML cs.LG

keywords tensordatafactorizationlearninglogisticmulti-relationalresultsvarious

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Tensor factorizations have become increasingly popular approaches for various learning tasks on structured data. In this work, we extend the RESCAL tensor factorization, which has shown state-of-the-art results for multi-relational learning, to account for the binary nature of adjacency tensors. We study the improvements that can be gained via this approach on various benchmark datasets and show that the logistic extension can improve the prediction results significantly.

Logistic Tensor Factorization for Multi-Relational Data

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