Abstract
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.
Original language | English |
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Title of host publication | 2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING : MLSP2007 |
Publisher | IEEE |
Publication date | 2007 |
Pages | 139-144 |
ISBN (Print) | 978-1-4244-1565-6 |
DOIs | |
Publication status | Published - 2007 |
Event | 2007 17th IEEE Workshop on Machine Learning for Signal Processing - Thessaloniki, Greece Duration: 27 Aug 2007 → 29 Aug 2007 Conference number: 17 https://ieeexplore.ieee.org/xpl/conhome/4414264/proceeding |
Conference
Conference | 2007 17th IEEE Workshop on Machine Learning for Signal Processing |
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Number | 17 |
Country/Territory | Greece |
City | Thessaloniki |
Period | 27/08/2007 → 29/08/2007 |
Internet address |
Bibliographical note
Copyright: 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEEKeywords
- Non-negative Matrix Factorization (NMF)
- Shift invariance