Probabilistic boolean tensor decomposition
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Authors
Colleges, School and Institutes
External organisations
- Department of Statistics, Oxford University, Oxford, UK
Abstract
Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules Boolean algebra. Here, we present its first probabilistic treatment. We facilitate scalable sampling-based posterior inference by exploitation of the combinatorial structure of the factor conditionals. Maximum a posteriori estimates consistently outperform existing non-probabilistic approaches. We show that our performance gains can partially be explained by convergence to solutions that occupy relatively large regions of the parameter space, as well as by implicit model averaging. Moreover, the Bayesian treatment facilitates model selection with much greater accuracy than the previously suggested minimum description length based approach. We investigate three real-world data sets. First, temporal interaction networks and behavioural data of university students demonstrate the inference of instructive latent patterns. Next, we decompose a tensor with more than 10 Billion data points, indicating relations of gene expression in cancer patients. Not only does this demonstrate scalability, it also provides an entirely novel perspective on relational properties of continuous data and, in the present example, on the molecular heterogeneity of cancer. Our implementation is available on GitHub: https://github.com/TammoR/LogicalFactorisationMachines
Details
Original language | English |
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Title of host publication | Volume 80 |
Subtitle of host publication | International Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden |
Editors | Jennifer Dy, Andreas Krause |
Publication status | Published - 10 Jul 2018 |
Event | The 35th International Conference on Machine Learning (ICML 2018) - Stockholmsmässan, Stockholm , Sweden Duration: 10 Jul 2018 → 15 Jul 2018 |
Publication series
Name | Proceedings of Machine Learning Research |
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Publisher | Proceedings of Machine Learning Research |
Volume | 80 |
ISSN (Electronic) | 1938-7228 |
Conference
Conference | The 35th International Conference on Machine Learning (ICML 2018) |
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Abbreviated title | ICML 2018 |
Country | Sweden |
City | Stockholm |
Period | 10/07/18 → 15/07/18 |