Shifted Non-negative Matrix Factorization
|Morten Mørup, Kristoffer H. Madsen, Lars K. Hansen|
|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.
|Type||Conference paper [With referee]|
|Conference||Machine Learning for Signal Processing (MLSP), IEEE Workshop on|
|BibTeX data|| [bibtex]|
|IMM Group(s)||Intelligent Signal Processing|