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Scalable probabilistic tensor factorization for binary and count data

Publication ,  Conference
Rai, P; Hu, C; Harding, M; Carin, L
Published in: IJCAI International Joint Conference on Artificial Intelligence
January 1, 2015

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.

Duke Scholars

Published In

IJCAI International Joint Conference on Artificial Intelligence

ISSN

1045-0823

Publication Date

January 1, 2015

Volume

2015-January

Start / End Page

3770 / 3776
 

Citation

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Rai, P., Hu, C., Harding, M., & Carin, L. (2015). Scalable probabilistic tensor factorization for binary and count data. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2015-January, pp. 3770–3776).
Rai, P., C. Hu, M. Harding, and L. Carin. “Scalable probabilistic tensor factorization for binary and count data.” In IJCAI International Joint Conference on Artificial Intelligence, 2015-January:3770–76, 2015.
Rai P, Hu C, Harding M, Carin L. Scalable probabilistic tensor factorization for binary and count data. In: IJCAI International Joint Conference on Artificial Intelligence. 2015. p. 3770–6.
Rai, P., et al. “Scalable probabilistic tensor factorization for binary and count data.” IJCAI International Joint Conference on Artificial Intelligence, vol. 2015-January, 2015, pp. 3770–76.
Rai P, Hu C, Harding M, Carin L. Scalable probabilistic tensor factorization for binary and count data. IJCAI International Joint Conference on Artificial Intelligence. 2015. p. 3770–3776.

Published In

IJCAI International Joint Conference on Artificial Intelligence

ISSN

1045-0823

Publication Date

January 1, 2015

Volume

2015-January

Start / End Page

3770 / 3776