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Scalable bayesian non-negative tensor factorization for massive count data

Publication ,  Conference
Hu, C; Rai, P; Chen, C; Harding, M; Carin, L
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2015

We present a Bayesian non-negative tensor factorization model for count-valued tensor data, and develop scalable inference algorithms (both batch and online) for dealing with massive tensors. Our generative model can handle overdispersed counts as well as infer the rank of the decomposition. Moreover, leveraging a reparameterization of the Poisson distribution as a multinomial facilitates conjugacy in the model and enables simple and efficient Gibbs sampling and variational Bayes (VB) inference updates, with a computational cost that only depends on the number of nonzeros in the tensor. The model also provides a nice interpretability for the factors; in our model, each factor corresponds to a “topic”. We develop a set of online inference algorithms that allow further scaling up the model to massive tensors, for which batch inference methods may be infeasible. We apply our framework on diverse real-world applications, such as multiway topic modeling on a scientific publications database, analyzing a political science data set, and analyzing a massive household transactions data set.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319235240

Publication Date

January 1, 2015

Volume

9285

Start / End Page

53 / 70

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Hu, C., Rai, P., Chen, C., Harding, M., & Carin, L. (2015). Scalable bayesian non-negative tensor factorization for massive count data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9285, pp. 53–70). https://doi.org/10.1007/978-3-319-23525-7_4
Hu, C., P. Rai, C. Chen, M. Harding, and L. Carin. “Scalable bayesian non-negative tensor factorization for massive count data.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9285:53–70, 2015. https://doi.org/10.1007/978-3-319-23525-7_4.
Hu C, Rai P, Chen C, Harding M, Carin L. Scalable bayesian non-negative tensor factorization for massive count data. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 53–70.
Hu, C., et al. “Scalable bayesian non-negative tensor factorization for massive count data.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9285, 2015, pp. 53–70. Scopus, doi:10.1007/978-3-319-23525-7_4.
Hu C, Rai P, Chen C, Harding M, Carin L. Scalable bayesian non-negative tensor factorization for massive count data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 53–70.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319235240

Publication Date

January 1, 2015

Volume

9285

Start / End Page

53 / 70

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences