Leveraging features and networks for probabilistic tensor decomposition

Piyush Rai, Yingjian Wang, Lawrence Carin

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

10 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAI Access Foundationminton@fetch.com
Number of pages7
ISBN (Print)9781577357025
StatePublished - Jun 1 2015
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09


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