Extrapolation of Group Proximity from Member Relations Using Embedding and Distribution Mapping

Hideaki MISAWA; Keiichi HORIO; Nobuo MOROTOMI; Kazumasa FUKUDA; Hatsumi TANIGUCHI

doi:10.1587/transinf.E95.D.804

Extrapolation of Group Proximity from Member Relations Using Embedding and Distribution Mapping

Hideaki MISAWA, Keiichi HORIO, Nobuo MOROTOMI, Kazumasa FUKUDA, Hatsumi TANIGUCHI

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In the present paper, we address the problem of extrapolating group proximities from member relations, which we refer to as the group proximity problem. We assume that a relational dataset consists of several groups and that pairwise relations of all members can be measured. Under these assumptions, the goal is to estimate group proximities from pairwise relations. In order to solve the group proximity problem, we present a method based on embedding and distribution mapping, in which all relational data, which consist of pairwise dissimilarities or dissimilarities between members, are transformed into vectorial data by embedding methods. After this process, the distributions of the groups are obtained. Group proximities are estimated as distances between distributions by distribution mapping methods, which generate a map of distributions. As an example, we apply the proposed method to document and bacterial flora datasets. Finally, we confirm the feasibility of using the proposed method to solve the group proximity problem.

Publication: IEICE TRANSACTIONS on Information Vol.E95-D No.3 pp.804-811

Publication Date: 2012/03/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E95.D.804

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

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Hideaki MISAWA, Keiichi HORIO, Nobuo MOROTOMI, Kazumasa FUKUDA, Hatsumi TANIGUCHI, "Extrapolation of Group Proximity from Member Relations Using Embedding and Distribution Mapping" in IEICE TRANSACTIONS on Information, vol. E95-D, no. 3, pp. 804-811, March 2012, doi: 10.1587/transinf.E95.D.804.
Abstract: In the present paper, we address the problem of extrapolating group proximities from member relations, which we refer to as the group proximity problem. We assume that a relational dataset consists of several groups and that pairwise relations of all members can be measured. Under these assumptions, the goal is to estimate group proximities from pairwise relations. In order to solve the group proximity problem, we present a method based on embedding and distribution mapping, in which all relational data, which consist of pairwise dissimilarities or dissimilarities between members, are transformed into vectorial data by embedding methods. After this process, the distributions of the groups are obtained. Group proximities are estimated as distances between distributions by distribution mapping methods, which generate a map of distributions. As an example, we apply the proposed method to document and bacterial flora datasets. Finally, we confirm the feasibility of using the proposed method to solve the group proximity problem.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.804/_p

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@ARTICLE{e95-d_3_804,
author={Hideaki MISAWA, Keiichi HORIO, Nobuo MOROTOMI, Kazumasa FUKUDA, Hatsumi TANIGUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Extrapolation of Group Proximity from Member Relations Using Embedding and Distribution Mapping},
year={2012},
volume={E95-D},
number={3},
pages={804-811},
abstract={In the present paper, we address the problem of extrapolating group proximities from member relations, which we refer to as the group proximity problem. We assume that a relational dataset consists of several groups and that pairwise relations of all members can be measured. Under these assumptions, the goal is to estimate group proximities from pairwise relations. In order to solve the group proximity problem, we present a method based on embedding and distribution mapping, in which all relational data, which consist of pairwise dissimilarities or dissimilarities between members, are transformed into vectorial data by embedding methods. After this process, the distributions of the groups are obtained. Group proximities are estimated as distances between distributions by distribution mapping methods, which generate a map of distributions. As an example, we apply the proposed method to document and bacterial flora datasets. Finally, we confirm the feasibility of using the proposed method to solve the group proximity problem.},
keywords={},
doi={10.1587/transinf.E95.D.804},
ISSN={1745-1361},
month={March},}

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TY - JOUR
TI - Extrapolation of Group Proximity from Member Relations Using Embedding and Distribution Mapping
T2 - IEICE TRANSACTIONS on Information
SP - 804
EP - 811
AU - Hideaki MISAWA
AU - Keiichi HORIO
AU - Nobuo MOROTOMI
AU - Kazumasa FUKUDA
AU - Hatsumi TANIGUCHI
PY - 2012
DO - 10.1587/transinf.E95.D.804
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2012
AB - In the present paper, we address the problem of extrapolating group proximities from member relations, which we refer to as the group proximity problem. We assume that a relational dataset consists of several groups and that pairwise relations of all members can be measured. Under these assumptions, the goal is to estimate group proximities from pairwise relations. In order to solve the group proximity problem, we present a method based on embedding and distribution mapping, in which all relational data, which consist of pairwise dissimilarities or dissimilarities between members, are transformed into vectorial data by embedding methods. After this process, the distributions of the groups are obtained. Group proximities are estimated as distances between distributions by distribution mapping methods, which generate a map of distributions. As an example, we apply the proposed method to document and bacterial flora datasets. Finally, we confirm the feasibility of using the proposed method to solve the group proximity problem.
ER -