Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MLSP.2018.8516924 | 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | Field | DocType |
probabilistic modeling,missing data,human brain microarray data,non-negative tensor factorization | Expression (mathematics),Pattern recognition,Matrix (mathematics),Computer science,Artificial intelligence,Non-negative matrix factorization,Probabilistic logic,Missing data,Tensor factorization,Model prediction,Bayesian probability | Conference |
ISSN | ISBN | Citations |
1551-2541 | 978-1-5386-5478-1 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesper Løve Hinrich | 1 | 4 | 2.44 |
| S. F. Nielsen | 2 | 6 | 1.34 |
| Kristoffer Hougaard Madsen | 3 | 145 | 18.74 |
| Morten Mørup | 4 | 704 | 51.29 |