Shifted Nonnegative Matrix Factorization 
Morten Mørup, Kristoffer H. Madsen, Lars K. Hansen

Abstract  Nonnegative 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 
Year  2007 
BibTeX data  [bibtex] 
IMM Group(s)  Intelligent Signal Processing 