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Parsimonious Segmentation of Time Series by Potts Models

  • Gerhard Winkler
  • Angela Kempe
  • Volkmar Liebscher
  • Olaf Wittich
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Potts Model Perfect Match Fractionation Curve Classical Criterion Primitive Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Gerhard Winkler
    • 1
  • Angela Kempe
    • 2
  • Volkmar Liebscher
    • 1
  • Olaf Wittich
    • 1
  1. 1.GSF — National Research Center for Environment and HealthInstitute of Biomathematics and BiometryNeuherberg/MünchenGermany
  2. 2.Graduate Programme Applied Algorithmic Mathematics, Center for Mathematical SciencesMunich University of TechnologyGarching

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