Bayesian multi-tensor factorization

Suleiman Khan*, Eemeli Leppäaho, Samuel Kaski

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

17 Citations (Scopus)

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.

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

Keywords

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

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