Bayesian multi-tensor factorization

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...

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Bibliographic Details
Published in:Machine learning 2016-11, Vol.105 (2), p.233-253
Main Authors: Khan, Suleiman A., Leppäaho, Eemeli, Kaski, Samuel
Format: Article
Language:English
Subjects:
Artificial Intelligence
Bayesian analysis
Computer Science
Control
Discriminant analysis
Factorization
Learning
Mathematical analysis
Mathematical models
Mechatronics
Natural Language Processing (NLP)
Neurology
Robotics
Simulation and Modeling
Tasks
Tensors
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Summary: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.
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-016-5563-y