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Clustering Classifiers Learnt from Local Datasets Based on Cosine Similarity

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Foundations of Intelligent Systems (ISMIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9384))

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Abstract

In this paper we present a new method to measure the degree of dissimilarity of a pair of linear classifiers. This method is based on the cosine similarity between the normal vectors of the hyperplanes of the linear classifiers. A significant advantage of this method is that it has a good interpretation and requires very little information to exchange among datasets. Evaluations on a synthetic dataset, a dataset from the UCI Machine Learning Repository, and facial expression datasets show that our method outperforms previous methods in terms of the normalized mutual information.

E. Suzuki—A part of this research was supported by Grant-in-Aid for Scientific Research 25280085 and 15K12100 from the Japanese Ministry of Education, Culture, Sports, Science and Technology.

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Notes

  1. 1.

    The same risk exists for relying on the density in the example space.

  2. 2.

    http://www.microsoft.com/en-us/kinectforwindows/.

  3. 3.

    http://msdn.microsoft.com/en-us/library/jj130970.aspx.

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Author information

Authors and Affiliations

  1. Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan

    Kaikai Zhao

  2. Department of Informatics, ISEE, Kyushu University, Fukuoka, Japan

    Einoshin Suzuki

Authors
  1. Kaikai Zhao
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  2. Einoshin Suzuki
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Corresponding author

Correspondence to Kaikai Zhao .

Editor information

Editors and Affiliations

  1. Computer Science, University of Bari, Bari, Italy

    Floriana Esposito

  2. Enssat, Lannion, France

    Olivier Pivert

  3. LISI-UFR d'Informatique, Université Claude Bernard Lyon 1, Villeurbanne Cedex, France

    Mohand-Said Hacid

  4. University of North Carolina, CHARLOTTE, North Carolina, USA

    Zbigniew W. Rás

  5. Dipartimento di Informatica, Università degli Studi di Bari, Bari, Italy

    Stefano Ferilli

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Zhao, K., Suzuki, E. (2015). Clustering Classifiers Learnt from Local Datasets Based on Cosine Similarity. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-25252-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25251-3

  • Online ISBN: 978-3-319-25252-0

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