@article{oai:kyutech.repo.nii.ac.jp:00006587,
 author = {Ishibashi, Hideaki and 石橋, 英朗 and Shinriki,  Ryota and Isogai,  Hirohisa and Furukawa, Tetsuo and 古川, 徹生},
 journal = {Lecture Notes in Computer Science},
 month = {Sep},
 note = {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., 23rd International Conference on Neural Information Processing, ICONIP 2016, October 16–21, 2016, Kyoto, Japan},
 pages = {459--466},
 title = {Multilevel–Multigroup Analysis Using a Hierarchical Tensor SOM Network},
 volume = {9949},
 year = {2016},
 yomi = {イシバシ, ヒデアキ and フルカワ, テツオ}
}