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Combining Mixture Models and Spectral Clustering for Data Partitioning

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Book cover Image Analysis and Recognition (ICIAR 2020)

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Abstract

Gaussian Mixture Models are widely used nowadays, thanks to the simplicity and efficiency of the Expectation-Maximization algorithm. However, determining the optimal number of components is tricky and, in the context of data partitioning, may differ from the actual number of clusters. We propose to apply a post-processing step by means of Spectral Clustering: it allows a clever merging of similar Gaussians thanks to the Bhattacharyya distance so that clusters of any shape are automatically discovered. The proposed method shows a significant improvement compared to the classical Gaussian Mixture clustering approach and promising results against well-known partitioning algorithms with respect to the number of parameters.

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Notes

  1. 1.

    https://github.com/deric/clustering-benchmark.

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Author information

Authors and Affiliations

  1. Université Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000, Grenoble, France

    Julien Muzeau, Maria Oliver-Parera, Patricia Ladret & Pascal Bertolino

Authors
  1. Julien Muzeau
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  2. Maria Oliver-Parera
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  3. Patricia Ladret
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  4. Pascal Bertolino
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Corresponding author

Correspondence to Julien Muzeau .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  3. University of Waterloo, Waterloo, ON, Canada

    Zhou Wang

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Muzeau, J., Oliver-Parera, M., Ladret, P., Bertolino, P. (2020). Combining Mixture Models and Spectral Clustering for Data Partitioning. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_6

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  • DOI: https://doi.org/10.1007/978-3-030-50516-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50515-8

  • Online ISBN: 978-3-030-50516-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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