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Extraction of dominant boundary set from high dimensional data

Research Project

Project/Area Number 18K11426
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 61030:Intelligent informatics-related
Research InstitutionThe University of Tokyo

Principal Investigator

inaba Mary  東京大学, 大学院情報理工学系研究科, 准教授 (60282711)

Project Period (FY) 2018-04-01 – 2023-03-31
Project Status Completed (Fiscal Year 2022)
Budget Amount *help
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2022: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2021: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Keywords最適化問題 / 幾何構造を利用する最適化 / グラフ構造を利用する最適化 / スカイライン問題 / SAT ソルバ / スカイライン / パレート最適 / サンプルの多様性 / クラスタリング問題 / 凸包 / ミニマ / 特徴抽出 / スカイライン計算 / 計算幾何 / データ抽出 / 境界集合
Outline of Final Research Achievements

In order to improve the performance of learning, prediction, and search using large-scale data, we proposed a method for extracting good sample data set. For example, random sampling rarely samples the "data with outstanding features" such as the maximum value of a certain feature value, and naive geometric approach to get such kind of data is to compute convex hull in the feature space, whose computing cost is extremely high especially in the high dimensional space. To tackle with this problem, we utilize BJR-tree structure, which is originally invented to solve the skyline problem using dominance relationship between a pair of data in the feature space. Roughly speaking, this approach is, to convert the geometric problem into graph (or, tree) problem, and the computational cost is not high comparing with computing convex hull in the high dimensional space. Experimental result shows that this approach is good for the low and the middle dimensional space.

Academic Significance and Societal Importance of the Research Achievements

BJR-tree 構造を用いて、pseudo-skyline 問題を効率よく解くことで、サンプル抽出することにより、「際立った特徴を持つデータ」をこぼすことなく、サンプル集合を得ることができる。この手法を、TCP/IP輻輳制御問題および、アプリケーション実行時に利用されるアドレス検出の強化学習実験で検証した結果、低中次元の特徴空間においては、概ね、予想通りの結果を得ることができたたが、高次元化すると、psuedo-skyline 集合が爆発的に増大してしまうため、多様性を用いた大規模データの探索問題に問題範囲を広げ、幾何構造に重ねる形でグラフ構造(本研究ではラティス)を用いた提案も行った。

Report

(6 results)
  • 2022 Annual Research Report   Final Research Report ( PDF )
  • 2021 Research-status Report
  • 2020 Research-status Report
  • 2019 Research-status Report
  • 2018 Research-status Report
  • Research Products

    (7 results)

All 2023 2022 2019 2018

All Journal Article (2 results) (of which Int'l Joint Research: 2 results,  Peer Reviewed: 2 results,  Open Access: 1 results) Presentation (5 results) (of which Int'l Joint Research: 3 results)

  • [Journal Article] BJR-tree: fast skyline computation algorithm using dominance relation-based tree structure2018

    • Author(s)
      Koizumi Kenichi、Eades Peter、Hiraki Kei、Inaba Mary
    • Journal Title

      International Journal of Data Science and Analytics

      Volume: 7 Issue: 1 Pages: 17-34

    • DOI

      10.1007/s41060-018-0098-x

    • Related Report
      2018 Research-status Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] Intelligent Image-Activated Cell Sorting2018

    • Author(s)
      Nitta N et al.
    • Journal Title

      Cell

      Volume: 175 Issue: 1 Pages: 266-276.e13

    • DOI

      10.1016/j.cell.2018.08.028

    • Related Report
      2018 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Presentation] Unleashing the potential of restart by detecting the search stagnation2023

    • Author(s)
      Yoichiro Iida, Tomohiro Sonobe, Mary Inaba
    • Organizer
      The 17th Learning and Intelligent Optimization (LION17)
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Diversification of Parallel Search of Portfolio SAT Solver by Search Similarity Index.2022

    • Author(s)
      Yoichiro Iida, Tomohiro Sonobe, Mary Inaba
    • Organizer
      PRICAI 2022: Trends in Artificial Intelligence - 19th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2022
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] シングルサーバを用いた100 Gbpsネットワークでのセキュアファイル転送2019

    • Author(s)
      下見淳一
    • Organizer
      IA 研究会
    • Related Report
      2019 Research-status Report
  • [Presentation] リソース制約条件を考慮した多目的最適化による高位合成2019

    • Author(s)
      濱崎福平
    • Organizer
      CPSY 研究会
    • Related Report
      2019 Research-status Report
  • [Presentation] Application of a fast skyline computation algorithm for serendipitous searching problems2018

    • Author(s)
      Koizumi Kenichi、Hiraki Kei、Inaba Mary
    • Organizer
      SPIE BIOS
    • Related Report
      2018 Research-status Report
    • Int'l Joint Research

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Published: 2018-04-23   Modified: 2024-01-30  

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