Slow features nonnegative matrix factorization for temporal data decomposition

Lazaros Zafeiriou, Symeon Nikitidis, Stefanos Zafeiriou, Maja Pantic

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    7 Citations (Scopus)
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    In this paper, we combine the principles of temporal slowness and nonnegative parts-based learning into a single framework that aims to learn slow varying parts-based representations of time varying sequences. We demonstrate that the proposed algorithm arises naturally by embedding the Slow Features Analysis trace optimization problem in the nonnegative subspace learning framework and derive novel multiplicative update rules for its optimization. The usefulness of the developed algorithm is demonstrated for unsupervised facial behaviour dynamics analysis on MMI database.
    Original languageUndefined
    Title of host publicationProceedings of IEEE International Conference on Image Processing (ICIP 2014)
    Place of PublicationUSA
    Number of pages5
    ISBN (Print)978-1-4799-5751-4
    Publication statusPublished - Oct 2014
    EventIEEE International Conference on Image Processing, ICIP 2014 - Paris, France
    Duration: 27 Oct 201430 Oct 2014

    Publication series

    PublisherIEEE Computer Society


    ConferenceIEEE International Conference on Image Processing, ICIP 2014
    Abbreviated titleICIP
    Internet address


    • HMI-HF: Human Factors
    • EWI-25823
    • EC Grant Agreement nr.: FP7/2007-2013
    • EC Grant Agreement nr.: FP7/288235
    • IR-95230
    • Nonnegative Matrix Factorization
    • Slow Features Analysis
    • METIS-309949
    • Facial behaviour dynamics analysis

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