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.
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Winkler, G., Kempe, A., Liebscher, V., Wittich, O. (2005). Parsimonious Segmentation of Time Series by Potts Models. In: Baier, D., Wernecke, KD. (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26981-9_34
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DOI: https://doi.org/10.1007/3-540-26981-9_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23221-6
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