Conference paper
Variational bayesian partially observed non-negative tensor factorization
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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2018 |
| Pages: | 1-6 |
| Proceedings: | 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing |
| ISBN: | 1538654776 , 1538654784 , 9781538654774 and 9781538654781 |
| Types: | Conference paper |
| DOI: | 10.1109/MLSP.2018.8516924 |
| ORCIDs: | Hinrich, Jesper Løve , Nielsen, Søren Føns Vind , Madsen, Kristoffer Hougaard and Mørup, Morten |
Human brain microarray data Missing data Non-negative tensor factorization Probabilistic modeling
Bayes methods Brain modeling Data models Gene expression Matrix decomposition Probabilistic logic Tensile stress VB-NMF-VB-NTF biology computing conventional NMF-NTF data analysis developed VB-NTF gene expression human brain microarray data inference mechanisms matrix decomposition missing data model prediction non-negative tensor factorization nonnegative tensor factorization partially observed data probabilistic modeling tensors variational Bayesian
