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German Conference on Pattern Recognition

DAGM 2015: Pattern Recognition pp 117–128Cite as

Copula Archetypal Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

Abstract

We present an extension of classical archetypal analysis (AA). It is motivated by the observation that classical AA is not invariant against strictly monotone increasing transformations. Establishing such an invariance is desirable since it makes AA independent of the chosen measure: representing a data set in meters or log(meters) should lead to approximately the same archetypes. The desired invariance is achieved by introducing a semi-parametric Gaussian copula. This ensures the desired invariance and makes AA more robust against outliers and missing values. Furthermore, our framework can deal with mixed discrete/continuous data, which certainly is the most widely encountered type of data in real world applications. Since the proposed extension is presented in form of a preprocessing step, updating existing classical AA models is especially effortless.

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Acknowledgements

This work was partially supported by the Swiss National Science Foundation, project 200021_146178: Copula Distributions in Machine Learning.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland

    Dinu Kaufmann, Sebastian Keller & Volker Roth

Authors
  1. Dinu Kaufmann
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  2. Sebastian Keller
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  3. Volker Roth
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Corresponding author

Correspondence to Dinu Kaufmann .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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© 2015 Springer International Publishing Switzerland

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Kaufmann, D., Keller, S., Roth, V. (2015). Copula Archetypal Analysis. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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