Abstract
This paper describes a method of multilevel–multigroup analysis based on a nonlinear multiway dimensionality reduction. To analyze a set of groups in terms of the probabilistic distribution of their constituent member data, the proposed method uses a hierarchical pair of tensor self-organizing maps (TSOMs), one for the member analysis and the other for the group analysis. This architecture enables more flexible analysis than ordinary parametric multilevel analysis, as it retains a high level of translatability supported by strong visualization. Furthermore, this architecture provides a consistent and seamless computation method for multilevel–multigroup analysis by integrating two different levels into a hierarchical tensor SOM network. The proposed method is applied to a dataset of football teams in a university league, and successfully visualizes the types of players that constitute each team as well as the differences or similarities between the teams.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Timmerman, M.E.: Multilevel component analysis. Br. J. Math. Stat. Psychol. 59, 301–320 (2006)
Eslami, A., Qannari, E.M., Kohler, A., Bougeard, S.: General overview of methods of analysis of multi-group datasets. RNTI 25, 108–123 (2013)
Friedman, J.H.: Exploratory projection pursuit. J. Am. Stat. Assoc. 82, 259–266 (1987)
Iwasaki, T., Furukawa, T.: Tensor SOM and tensor GTM: nonlinear tensor analysis by topographic mappings. Neural Netw. 77, 107–125 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Ishibashi, H., Shinriki, R., Isogai, H., Furukawa, T. (2016). Multilevel–Multigroup Analysis Using a Hierarchical Tensor SOM Network. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-46675-0_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46674-3
Online ISBN: 978-3-319-46675-0
eBook Packages: Computer ScienceComputer Science (R0)