Extrapolation of Group Proximity from Member Relations Using Embedding and Distribution Mapping
| Autor: | Keiichi Horio, Kazumasa Fukuda, Hideaki Misawa, Hatsumi Taniguchi, Nobuo Morotomi |
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| Rok vydání: | 2012 |
| Předmět: |
Theoretical computer science
Group (mathematics) Computer science Relational database Extrapolation Process (computing) computer.software_genre Artificial Intelligence Hardware and Architecture Embedding Order (group theory) Pairwise comparison Computer Vision and Pattern Recognition Data mining Electrical and Electronic Engineering computer Software Distribution (differential geometry) |
| Zdroj: | IEICE Transactions on Information and Systems. :804-811 |
| ISSN: | 1745-1361 0916-8532 |
| DOI: | 10.1587/transinf.e95.d.804 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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