Bayesian multi-tensor factorization

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Abstract

We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors. The research problem generalizes the joint matrix–tensor factorization problem to arbitrary sets of tensors of any depth, including matrices, can be interpreted as unsupervised multi-view learning from multiple data tensors, and can be generalized to relax the usual trilinear tensor factorization assumptions. The result is a factorization of the set of tensors into factors shared by any subsets of the tensors, and factors private to individual tensors. We demonstrate the performance against existing baselines in multiple tensor factorization tasks in structural toxicogenomics and functional neuroimaging.

Details

Original languageEnglish
Pages (from-to)233-253
Number of pages21
JournalMachine Learning
Volume105
Issue number2
Early online date10 Jun 2016
Publication statusPublished - Nov 2016
MoE publication typeA1 Journal article-refereed

    Research areas

  • Bayesian factorization, CANDECOMP/PARAFAC, Coupled matrix tensor factorization, Factor analysis, Tensor factorization

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