Modelling relational data using Bayesian clustered tensor factorization
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3863-modelling-relational-data-using-bayesian-clustered-tensor-factorization.pdf
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Author(s)
Sutskever, Ilya
Tenenbaum, Joshua B.
Salakhutdinov, Ruslan
Date Issued
December 2009
Journal
Advances in Neural Information Processing Systems 22 (NIPS 2009)
Publisher
Neural Information Processing Systems Foundation
Citation
Sutskever, Ilya, Ruslan Salakhutdinov and Joshua B. Tenenbaum. "Modelling Relational Data using Bayesian Clustered Tensor Factorization." Advances in Neural Information Processing Systems 22 (NIPS 2009), Vancouver, British Columbia, Canada, 6-10 December, 2009. © 2009 Neural Information Processing Systems Foundation
Version
Final published version
Abstract
We consider the problem of learning probabilistic models for complex relational structures between various types of objects. A model can help us "understand" a dataset of relational facts in at least two ways, by finding interpretable structure in the data, and by supporting predictions, or inferences about whether particular unobserved relations are likely to be true. Often there is a tradeoff between these two aims: cluster-based models yield more easily interpretable representations, while factorization-based approaches have given better predictive performance on large data sets. We introduce the Bayesian Clustered Tensor Factorization (BCTF) model, which embeds a factorized representation of relations in a nonparametric Bayesian clustering framework. Inference is fully Bayesian but scales well to large data sets. The model simultaneously discovers interpretable clusters and yields predictive performance that matches or beats previous probabilistic models for relational data.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://papers.nips.cc/paper/3863-modelling-relational-data-using-bayesian-clustered-tensor-factorization
