Extrapolation of group proximity from member relations using embedding and distribution mapping

Hideaki Misawa, Keiichi Horio, Nobuo Morotomi, Kazumasa Fukuda, Hatsumi Taniguchi

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)804-811
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE95-D
Issue number3
DOIs
Publication statusPublished - Jan 1 2012

Fingerprint

Extrapolation

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Cite this

Extrapolation of group proximity from member relations using embedding and distribution mapping. / Misawa, Hideaki; Horio, Keiichi; Morotomi, Nobuo; Fukuda, Kazumasa; Taniguchi, Hatsumi.

In: IEICE Transactions on Information and Systems, Vol. E95-D, No. 3, 01.01.2012, p. 804-811.

Research output: Contribution to journalArticle

Misawa, Hideaki ; Horio, Keiichi ; Morotomi, Nobuo ; Fukuda, Kazumasa ; Taniguchi, Hatsumi. / Extrapolation of group proximity from member relations using embedding and distribution mapping. In: IEICE Transactions on Information and Systems. 2012 ; Vol. E95-D, No. 3. pp. 804-811.
@article{3a563f6f16a34f96a277e8a92a8d461b,
title = "Extrapolation of group proximity from member relations using embedding and distribution mapping",
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.",
author = "Hideaki Misawa and Keiichi Horio and Nobuo Morotomi and Kazumasa Fukuda and Hatsumi Taniguchi",
year = "2012",
month = "1",
day = "1",
doi = "10.1587/transinf.E95.D.804",
language = "English",
volume = "E95-D",
pages = "804--811",
journal = "IEICE Transactions on Information and Systems",
issn = "0916-8532",
publisher = "一般社団法人電子情報通信学会",
number = "3",

}

TY - JOUR

T1 - Extrapolation of group proximity from member relations using embedding and distribution mapping

AU - Misawa, Hideaki

AU - Horio, Keiichi

AU - Morotomi, Nobuo

AU - Fukuda, Kazumasa

AU - Taniguchi, Hatsumi

PY - 2012/1/1

Y1 - 2012/1/1

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=84857816319&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84857816319&partnerID=8YFLogxK

U2 - 10.1587/transinf.E95.D.804

DO - 10.1587/transinf.E95.D.804

M3 - Article

AN - SCOPUS:84857816319

VL - E95-D

SP - 804

EP - 811

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

SN - 0916-8532

IS - 3

ER -