Conference paper
Shifted Non-negative Matrix Factorization
Non-negative matrix factorization (NMF) has become a widely used blind source separation technique due to its part based representation and ease of interpretability. We currently extend the NMF model to allow for delays between sources and sensors. This is a natural extension for spectrometry data where a shift in onset of frequency profile can be induced by the Doppler effect.
However, the model is also relevant for biomedical data analysis where the sources are given by compound intensities over time and the onset of the profiles have different delays to the sensors. A simple algorithm based on multiplicative updates is derived and it is demonstrated how the algorithm correctly identifies the components of a synthetic data set.
Matlab implementation of the algorithm and a demonstration data set is available.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2007 |
| Pages: | 139-144 |
| Proceedings: | 2007 17th IEEE Workshop on Machine Learning for Signal Processing |
| Journal subtitle: | Mlsp2007 |
| ISBN: | 1424415659 , 1424415667 , 9781424415656 and 9781424415663 |
| ISSN: | 21610363 and 15512541 |
| Types: | Conference paper |
| DOI: | 10.1109/MLSP.2007.4414296 |
| ORCIDs: | Mørup, Morten , Madsen, Kristoffer Hougaard and Hansen, Lars Kai |
Bioinformatics Biosensors Deconvolution Delay effects Doppler effect Independent component analysis Informatics Interpolation Mathematical model Matlab implementation Matrix decomposition Maximum likelihood estimation biomedical data analysis blind source separation blind source separation technique matrix decomposition shifted nonnegative matrix factorization signal representation singal representation spectrometry data
