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Hierarchical Tensor SOM Network for Multilevel–Multigroup Analysis

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Abstract

The aim of this work is to develop a visualization method for multilevel–multigroup analysis based on a multiway nonlinear dimensionality reduction. The task of the method is to visualize what kinds of members each group is composed and to visualize the similarity between the groups in terms of probability distribution of constituent members. To achieve the task, the proposed method consists of hierarchically coupled tensor self-organizing maps, corresponding to the member/group level. This architecture enables more flexible analysis than ordinary parametric multilevel analysis, as it retains a high level of interpretability supported by strong visualization. We applied the proposed method to one benchmark dataset and two practical datasets: one is the survey data on the football players belonging to different teams and the other is the employee survey data belonging to different departments in a company. Our method successfully visualizes the types of the members that constitute each group as well as visualizes the differences or similarities between the groups.

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Notes

  1. Actually \(f_{lm}\) becomes a scalar here, but we call it a reference vector for consistent explanation.

  2. http://www.brain.kyutech.ac.jp/~furukawa/tsom/multilevel.

  3. http://www.kamishima.net/sushi/.

  4. 50F and 50M include 60s people.

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Acknowledgements

We would like to thank Prof. Isogai and Dr. Shinriki, Kyushu Institute of Technology, for allowing us to use football player data. We also would like to thank Dr. Iwasaki for much assistance in developing analysis tools and useful discussion.

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Authors and Affiliations

  1. Department of Human Intelligence Systems, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, 808-0196, Japan

    Hideaki Ishibashi & Tetsuo Furukawa

Authors
  1. Hideaki Ishibashi
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  2. Tetsuo Furukawa
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Correspondence to Tetsuo Furukawa.

Additional information

This work is supported by Okawa Foundation for Information and Telecommunications.

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Ishibashi, H., Furukawa, T. Hierarchical Tensor SOM Network for Multilevel–Multigroup Analysis. Neural Process Lett 47, 1011–1025 (2018). https://doi.org/10.1007/s11063-017-9643-1

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  • DOI: https://doi.org/10.1007/s11063-017-9643-1

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