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Generative Histogram-Based Model Using Unsupervised Learning

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Neural Information Processing (ICONIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11955))

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Abstract

This paper presents a new generative unsupervised learning algorithm based on a representation of the clusters distribution by histograms. The main idea is to reduce the model complexity through cluster-defined projections of the data on independent axes. The results show that the proposed approach performs efficiently compared with other algorithms. In addition, it is more efficient to generate new instances with the same distribution than the training data.

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Acknowledgements

This work was supported in part by the Pro-TEXT project (No ANR-18-CE23-0024) financed by the ANR (Agence Nationale de la Recherche).

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Authors and Affiliations

  1. LORIA, UMR-CNRS 7503, University of Lorraine, Nancy, France

    Parisa Rastin & Thierry Couronne

  2. LIPN, UMR-CNRS 7030, UP13, Sorbonne Paris Cité, Villetaneuse, France

    Guénaël Cabanes, Younès Bennani & Thierry Couronne

  3. Dipartimento Matematica e Fisica, Università della Campania Luigi Vanvitelli, Naples, Italy

    Rosanna Verde & Thierry Couronne

Authors
  1. Parisa Rastin
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  2. Guénaël Cabanes
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  3. Rosanna Verde
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  4. Younès Bennani
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  5. Thierry Couronne
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Corresponding author

Correspondence to Parisa Rastin .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Rastin, P., Cabanes, G., Verde, R., Bennani, Y., Couronne, T. (2019). Generative Histogram-Based Model Using Unsupervised Learning. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11955. Springer, Cham. https://doi.org/10.1007/978-3-030-36718-3_53

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  • DOI: https://doi.org/10.1007/978-3-030-36718-3_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36717-6

  • Online ISBN: 978-3-030-36718-3

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