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2021 Fiscal Year Final Research Report

Three-way three-mode dimensional data analysis for biometric data

Research Project

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Project/Area Number 17K12797
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeMulti-year Fund
Research Field Library and information science/Humanistic social informatics
Research InstitutionDoshisha University (2020-2021)
Wakayama Medical University (2017-2019)

Principal Investigator

Tanioka Kensuke  同志社大学, 生命医科学部, 助教 (40782818)

Project Period (FY) 2017-04-01 – 2022-03-31
Keywordsクラスタリング法 / スパース推定 / 次元縮約
Outline of Final Research Achievements

We have developed a data analysis method to simultaneously grasp the classification structure of subjects and related features from the tuple (subject, variable, condition), which is called Three-way three-mode data. We also developed a dimensional reduction clustering method that can identify regions of difference between conditions even in noisy data. We also developed a sparse dimensionality reduction clustering method based on path analysis. As a result of this research, seven papers have been published, one is in submission and one is in preparation for submission.

Free Research Field

多変量データ解析

Academic Significance and Societal Importance of the Research Achievements

情報技術の発達に伴うデータの大規模複雑化により,解析者はより複雑なデータを処理する必要に迫られている.3相3元データはそのような大規模複雑データのひとつであり,対象×変量×条件の3組間の構造を表現したデータであり,各固有の特徴を解釈することは困難である.今回開発した次元縮約クラスタリング法を用いることで,変量や条件固有の特徴を把握することが可能となる.また,パス解析の考えやスパース推定も加えた方法によって解析者の仮説をデータから検証し,解釈すべき項目数を削減することができることから,より解析者が容易に当該構造を解釈することが可能となる.

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Published: 2023-01-30  

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