Parsimonious segmentation of time series by Potts models

G. Winkler, A. Kempe, V. Liebscher, O. Wittich

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    Typical problems in the analysis of data sets like time-series or images crucially rely on the extraction of primitive features based on segmentation. Variational approaches are a popular and convenient framework in which such problems can be studied. We focus on Potts models as simple nontrivial instances. The discussion proceeds along two data sets from brain mapping and functional genomics.
    Original languageEnglish
    Title of host publicationInnovations in Classification, Data Science, and Information Systems (Proceedings of the 27th Annual Conference of the Gesellschaft für Klassifikation e.V., Cottbus, Germany, March 12-14, 2003), Part II
    EditorsD. Baier, K.D. Warnecke
    Place of PublicationBerlin
    PublisherSpringer
    Chapter34
    Pages295-302
    Number of pages9
    ISBN (Electronic)978-3-540-26981-6
    ISBN (Print)3-540-23221-4, 978-3-540-23221-6
    DOIs
    Publication statusPublished - 2005

    Publication series

    NameStudies in Classification, Data Analysis, and Knowledge Organization

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