Hierarchical Tensor Manifold Modeling for Multi-Group Analysis
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E101-A No.11 pp.1745-1755
- Publication Date
- 2018/11/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E101.A.1745
- Type of Manuscript
- Special Section PAPER (Special Section on Smart Multimedia & Communication Systems)
- Category
- Neural Networks and Bioengineering
Authors
Hideaki ISHIBASHI
the Institute of Statistical Mathematics
Masayoshi ERA
Kyushu Institute of Technology
Tetsuo FURUKAWA
Kyushu Institute of Technology
Keyword
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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},}
Copy
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 -
English
