Leveraging features and networks for probabilistic tensor decomposition

Published

Conference Paper

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We present a probabilistic model for tensor decomposition where one or more tensor modes may have sideinformation about the mode entities in form of their features and/or their adjacency network. We consider a Bayesian approach based on the Canonical PARAFAC (CP) decomposition and enrich this single-layer decomposition approach with a two-layer decomposition. The second layer fits a factor model for each layer-one factor matrix and models the factor matrix via the mode entities' features and/or the network between the mode entities. The second-layer decomposition of each factor matrix also learns a binary latent representation for the entities of that mode, which can be useful in its own right. Our model can handle both continuous as well as binary tensor observations. Another appealing aspect of our model is the simplicity of the model inference, with easy-to-sample Gibbs updates. We demonstrate the results of our model on several benchmarks datasets, consisting of both real and binary tensors.

Duke Authors

Cited Authors

  • Rai, P; Wang, Y; Carin, L

Published Date

  • June 1, 2015

Published In

  • Proceedings of the National Conference on Artificial Intelligence

Volume / Issue

  • 4 /

Start / End Page

  • 2942 - 2948

International Standard Book Number 13 (ISBN-13)

  • 9781577357025

Citation Source

  • Scopus