Scalable bayesian low-rank decomposition of incomplete multiway tensors

Conference Paper

We present a scalable Bayesian framework for low-rank decomposition of multiway tensor data with missing observations. The key issue of pre-specifying the rank of the decomposition is sidestepped in a principled manner using a multiplicative gamma process prior. Both continuous and binary data can be analyzed under the framework, in a coherent way using fully conjugate Bayesian analysis. In particular, the analysis in the non-conjugate binary case is facilitated via the use of the Pólya-Gamma sampling strategy which elicits closed-form Gibbs sampling updates. The resulting samplers are efficient and enable us to apply our framework to large-scale problems, with time-complexity that is linear in the number of observed entries in the tensor. This is especially attractive in analyzing very large but sparsely observed tensors with very few known entries. Moreover, our method admits easy extension to the supervised setting where entities in one or more tensor modes have labels. Our method outperforms several state-of-the-art tensor decomposition methods on various synthetic and benchmark real-world datasets.

Duke Authors

Cited Authors

  • Rai, P; Wang, Y; Guo, S; Chen, G; Dunson, D; Carin, L

Published Date

  • January 1, 2014

Published In

  • 31st International Conference on Machine Learning, Icml 2014

Volume / Issue

  • 5 /

Start / End Page

  • 3810 - 3820

International Standard Book Number 13 (ISBN-13)

  • 9781634393973

Citation Source

  • Scopus