Scalable probabilistic tensor factorization for binary and count data

Piyush Rai, Changwei Hu, Matthew Harding, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

Tensor factorization methods provide a useful way to extract latent factors from complex multirelational data, and also for predicting missing data. Developing tensor factorization methods for massive tensors, especially when the data are binary- or count-valued (which is true of most real-world tensors), however, remains a challenge. We develop a scalable probabilistic tensor factorization framework that enables us to perform efficient factorization of massive binary and count tensor data. The framework is based on (i) the Pólya-Gamma augmentation strategy which makes the model fully locally conjugate and allows closed-form parameter updates when data are binary- or count-valued; and (ii) an efficient online Expectation Maximization algorithm, which allows processing data in small mini-batches, and facilitates handling massive tensor data. Moreover, various types of constraints on the factor matrices (e.g., sparsity, non-negativity) can be incorporated under the proposed framework, providing good interpretability, which can be useful for qualitative analyses of the results. We apply the proposed framework on analyzing several binary-and count-valued real-world data sets.
Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial IntelligenceThomas.schiex@toulouse.inra.fr
Pages3770-3776
Number of pages7
ISBN (Print)9781577357384
StatePublished - Jan 1 2015
Externally publishedYes

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