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Patchworking Multiple Pairwise Distances for Learning with Distance Matrices

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Book cover Latent Variable Analysis and Signal Separation (LVA/ICA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9237))

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Abstract

A classification framework using only a set of distance matrices is proposed. The proposed algorithm can learn a classifier only from a set of distance matrices or similarity matrices, hence applicable to structured data, which do not have natural vector representation such as time series and graphs. Random forest is used to explore ideal feature representation based on the distance between points defined by a set of given distance matrices. The effectiveness of the proposed method is evaluated through experiments with point process data and graph structured data.

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Notes

  1. 1.

    So, we need to access to the distance functions.

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Acknowledgements

Part of this work is supported by KAKENHI No.26120504, 25870811, and 25120009.

Author information

Authors and Affiliations

  1. University of Tsukuba, 1–1–1 Tennoudai, Tsukuba, Ibaraki, 305–8573, Japan

    Hideitsu Hino

  2. Waseda University, 3–4–1 Ohkubo, Tokyo, Shinjuku-ku, 169–8555, Japan

    Ken Takano, Yuki Yoshikawa & Noboru Murata

Authors
  1. Ken Takano
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  2. Hideitsu Hino
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  3. Yuki Yoshikawa
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  4. Noboru Murata
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Corresponding author

Correspondence to Hideitsu Hino .

Editor information

Editors and Affiliations

  1. Inria, Villers-les-Nancy, France

    Emmanuel Vincent

  2. Tel Aviv University, Tel-Aviv, Israel

    Arie Yeredor

  3. Technical University of Libere, Liberec, Czech Republic

    Zbyněk Koldovský

  4. The Czech Academy of Sciences, Prague, Czech Republic

    Petr Tichavský

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Takano, K., Hino, H., Yoshikawa, Y., Murata, N. (2015). Patchworking Multiple Pairwise Distances for Learning with Distance Matrices. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_33

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  • DOI: https://doi.org/10.1007/978-3-319-22482-4_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22481-7

  • Online ISBN: 978-3-319-22482-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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