Shifted Non-negative Matrix Factorization

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    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 languageEnglish
    Title of host publication2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING : MLSP2007
    PublisherIEEE
    Publication date2007
    Pages139-144
    ISBN (Print)978-1-4244-1565-6
    DOIs
    Publication statusPublished - 2007
    Event2007 IEEE International Workshop on Machine Learning for Signal Processing - Thessaloniki, Greece
    Duration: 27 Aug 200729 Aug 2007
    http://mlsp2007.conwiz.dk/

    Workshop

    Workshop2007 IEEE International Workshop on Machine Learning for Signal Processing
    CountryGreece
    CityThessaloniki
    Period27/08/200729/08/2007
    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 IEEE

    Keywords

    • Non-negative Matrix Factorization (NMF)
    • Shift invariance

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