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Variational Bayesian partially observed non-negative tensor factorization

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Non-negative matrix and tensor factorization (NMF/NTF) have become important tools for extracting part based representations in data. It is however unclear when an NMF or NTF approach is most suited for data and how reliably the models predict when trained on partially observed data. We presently extend a recently proposed variational Bayesian NMF (VB-NMF) to non-negative tensor factorization (VB-NTF) for partially observed data. This admits bi- and multi-linear structure quantification considering both model prediction and evidence. We evaluate the developed VB-NTF on synthetic and a real dataset of gene expression in the human brain and contrast the performance to VB-NMF and conventional NMF/NTF. We find that the gene expressions are better accounted for by VB-NMF than VB-NTF and that VB-NMF/VB-NTF more robustly handle partially observed data than conventional NMF/NTF. In particular, probabilistic modeling is beneficial when large amounts of data is missing and/or the model order over-specified.

Original languageEnglish
Title of host publication2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings
Volume2018-September
PublisherIEEE Computer Society Press
Publication date2018
Pages1-6
Article number8516924
ISBN (Electronic)9781538654774
DOIs
Publication statusPublished - 2018
Event28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Aalborg, Denmark
Duration: 17 Sep 201820 Sep 2018

Conference

Conference28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018
LandDenmark
ByAalborg
Periode17/09/201820/09/2018
SponsorIEEE Signal Processing Society

Event

28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018

17/09/201820/09/2018

Aalborg, Denmark

Event: Conference

    Research areas

  • Human brain microarray data, Missing data, Non-negative tensor factorization, Probabilistic modeling

ID: 56438196