The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Le but de ce travail est de développer une méthode d'analyse simultanée de plusieurs groupes et de leurs membres basée sur une modélisation de variétés tensorielles hiérarchiques. La méthode est particulièrement conçue pour analyser plusieurs équipes, telles que des équipes sportives et des équipes commerciales. La méthode proposée représente les données des membres en utilisant une variété non linéaire pour chaque équipe, puis ces variétés sont ensuite modélisées à l'aide d'une autre variété non linéaire dans l'espace modèle. À cette fin, la méthode estime le rôle de chaque membre de l'équipe et découvre les correspondances entre les membres qui jouent des rôles similaires dans différentes équipes. La méthode proposée a été appliquée aux données d'une ligue de basket-ball et a démontré la capacité de découverte de connaissances à partir des statistiques des joueurs. Nous avons également démontré que la méthode pouvait être utilisée comme outil général d’analyse multi-niveaux multi-groupes en l’appliquant aux données marketing.
Hideaki ISHIBASHI
the Institute of Statistical Mathematics
Masayoshi ERA
Kyushu Institute of Technology
Tetsuo FURUKAWA
Kyushu Institute of Technology
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Hideaki ISHIBASHI, Masayoshi ERA, Tetsuo FURUKAWA, "Hierarchical Tensor Manifold Modeling for Multi-Group Analysis" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 11, pp. 1745-1755, November 2018, doi: 10.1587/transfun.E101.A.1745.
Abstract: The aim of this work is to develop a method for the simultaneous analysis of multiple groups and their members based on hierarchical tensor manifold modeling. The method is particularly designed to analyze multiple teams, such as sports teams and business teams. The proposed method represents members' data using a nonlinear manifold for each team, and then these manifolds are further modeled using another nonlinear manifold in the model space. For this purpose, the method estimates the role of each member in the team, and discovers correspondences between members that play similar roles in different teams. The proposed method was applied to basketball league data, and it demonstrated the ability of knowledge discovery from players' statistics. We also demonstrated that the method could be used as a general tool for multi-level multi-group analysis by applying it to marketing data.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1745/_p
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@ARTICLE{e101-a_11_1745,
author={Hideaki ISHIBASHI, Masayoshi ERA, Tetsuo FURUKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Hierarchical Tensor Manifold Modeling for Multi-Group Analysis},
year={2018},
volume={E101-A},
number={11},
pages={1745-1755},
abstract={The aim of this work is to develop a method for the simultaneous analysis of multiple groups and their members based on hierarchical tensor manifold modeling. The method is particularly designed to analyze multiple teams, such as sports teams and business teams. The proposed method represents members' data using a nonlinear manifold for each team, and then these manifolds are further modeled using another nonlinear manifold in the model space. For this purpose, the method estimates the role of each member in the team, and discovers correspondences between members that play similar roles in different teams. The proposed method was applied to basketball league data, and it demonstrated the ability of knowledge discovery from players' statistics. We also demonstrated that the method could be used as a general tool for multi-level multi-group analysis by applying it to marketing data.},
keywords={},
doi={10.1587/transfun.E101.A.1745},
ISSN={1745-1337},
month={November},}
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TY - JOUR
TI - Hierarchical Tensor Manifold Modeling for Multi-Group Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1745
EP - 1755
AU - Hideaki ISHIBASHI
AU - Masayoshi ERA
AU - Tetsuo FURUKAWA
PY - 2018
DO - 10.1587/transfun.E101.A.1745
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2018
AB - The aim of this work is to develop a method for the simultaneous analysis of multiple groups and their members based on hierarchical tensor manifold modeling. The method is particularly designed to analyze multiple teams, such as sports teams and business teams. The proposed method represents members' data using a nonlinear manifold for each team, and then these manifolds are further modeled using another nonlinear manifold in the model space. For this purpose, the method estimates the role of each member in the team, and discovers correspondences between members that play similar roles in different teams. The proposed method was applied to basketball league data, and it demonstrated the ability of knowledge discovery from players' statistics. We also demonstrated that the method could be used as a general tool for multi-level multi-group analysis by applying it to marketing data.
ER -