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Knowledge extraction from large-scale sequence data by combining pattern mining and sparse modeling

Research Project

Project/Area Number 17H04694
Research Category

Grant-in-Aid for Young Scientists (A)

Allocation TypeSingle-year Grants
Research Field Intelligent informatics
Research InstitutionNagoya Institute of Technology

Principal Investigator

Karasuyama Masayuki  名古屋工業大学, 工学(系)研究科(研究院), 准教授 (40628640)

Project Period (FY) 2017-04-01 – 2021-03-31
Project Status Completed (Fiscal Year 2020)
Budget Amount *help
¥21,580,000 (Direct Cost: ¥16,600,000、Indirect Cost: ¥4,980,000)
Fiscal Year 2020: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2019: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2017: ¥9,230,000 (Direct Cost: ¥7,100,000、Indirect Cost: ¥2,130,000)
Keywords機械学習 / 疎性モデリング / パターンマイニング / 構造データ / 系列データ / グラフデータ / マイニング / 凸最適化
Outline of Final Research Achievements

Because of development of a variety of sensor devices and increase of the capacity of storage devices, a significance of data-driven approaches to extracting useful knowledge from accumulated data have been widely recognized. In this study, we consider identifying important substructures in sequence or more complicated graph data, which has a connected structure inside a data instance. Since combinatorially many possible substructures exist, we propose efficient pruning algorithms that removes unnecessary patterns. Our framework is based on an optimization theory, and thus, the optimality of the resulting identified substructures can be guaranteed.

Academic Significance and Societal Importance of the Research Achievements

機械学習技術の注目が高まるつれ,その解釈性は大きな社会的関心事となっている.本課題で扱うような系列やグラフは,時系列データや化合物などの科学データで単純な数値テーブルでは表現できないデータの表現方法として広く定着しているものである.そのため,多様化するデータ駆動解析において,本課題で扱ったような問題設定は今後ますます顕在化すると考えられる.一方で,組み合わせ的に爆発する部分構造に対し,最適性を保証しつつ学習する枠組みはほとんど研究がなく,本研究の独自性・意義を示すものである.

Report

(5 results)
  • 2020 Annual Research Report   Final Research Report ( PDF )
  • 2019 Annual Research Report
  • 2018 Annual Research Report
  • 2017 Annual Research Report

Research Products

(14 results)

All 2021 2020 2019 2018 2017

All Journal Article (5 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 5 results,  Open Access: 3 results) Presentation (9 results) (of which Int'l Joint Research: 4 results)

  • [Journal Article] Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design2021

    • Author(s)
      Inoue Keiichi、Karasuyama Masayuki、Nakamura Ryoko、Konno Masae、Yamada Daichi、Mannen Kentaro、Nagata Takashi、Inatsu Yu、Yawo Hiromu、Yura Kei、Beja Oded、Kandori Hideki、Takeuchi Ichiro
    • Journal Title

      Communications Biology

      Volume: 4 Pages: 362-362

    • DOI

      10.1038/s42003-021-01878-9

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Stat-DSM: Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction2020

    • Author(s)
      V. N. L. Duy, T. Sakuma, T. Ishiyama, H. Toda, K. Arai, M. Karasuyama, Y. Okubo, M. Sunaga, H. Hanada, Y. Tabei, I. Takeuchi
    • Journal Title

      IEEE Transactions on Knowledge and Data Engineering

      Volume: -

    • DOI

      10.1109/tkde.2020.2994344

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Safe Triplet Screening for Distance Metric Learning2019

    • Author(s)
      Yoshida Tomoki、Takeuchi Ichiro、Karasuyama Masayuki
    • Journal Title

      Neural Computation

      Volume: 31 Pages: 2432-2491

    • DOI

      10.1162/neco_a_01240

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Efficient Learning Algorithm for Sparse SubSequence Pattern-based Classication and Applications to Comparative Animal Trajectory Data Analysis2019

    • Author(s)
      Takuto Sakuma, Kazuya Nishi, Kaoru Kishimoto, Kazuya Nakagawa, Masayuki Karasuyama, Yuta Umezu, Shinsuke Kajioka, Shuhei J. Yamazaki, Koutarou D. Kimura, Sakiko Matsumoto, Ken Yoda, Matasaburo Fukutomi, Hisashi Shidara, Hiroto Ogawa, Ichiro Takeuchi
    • Journal Title

      Advanced Robotics

      Volume: 33 Pages: 134-152

    • DOI

      10.1080/01691864.2019.1571438

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Exploring a potential energy surface by machine learning for characterizing atomic transport2018

    • Author(s)
      Kanamori Kenta、Toyoura Kazuaki、Honda Junya、Hattori Kazuki、Seko Atsuto、Karasuyama Masayuki、Shitara Kazuki、Shiga Motoki、Kuwabara Akihide、Takeuchi Ichiro
    • Journal Title

      Physical Review B

      Volume: 97 Pages: 125124-125124

    • DOI

      10.1103/physrevb.97.125124

    • Related Report
      2017 Annual Research Report
    • Peer Reviewed
  • [Presentation] 属性区間付きグラフを用いた予測グラフマイニング2021

    • Author(s)
      朝日陽向, 烏山昌幸
    • Organizer
      情報論的学習理論と機械学習研究会
    • Related Report
      2020 Annual Research Report
  • [Presentation] 予測パターンマイニングにおける非単調性特徴量のためのSafe Pattern Pruning2021

    • Author(s)
      羽川晟史, 烏山昌幸
    • Organizer
      ニューロコンピューティング研究会
    • Related Report
      2020 Annual Research Report
  • [Presentation] Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction2019

    • Author(s)
      D. V. N. Le, T. Sakuma, T. Ishiyama, H. Toda, K. Arai, M. Karasuyama, Y. Okubo, M. Sunaga, Y. Tabei, I. Takeuchi
    • Organizer
      the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
    • Related Report
      2019 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining2019

    • Author(s)
      T. Yoshida, I. Takeuchi, and M. Karasuyama
    • Organizer
      The 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Safe Triplet Screening for Distance Metric Learning2018

    • Author(s)
      T. Yoshida, I. Takeuchi, and M. Karasuyama
    • Organizer
      The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research
  • [Presentation] 部分グラフに基づくグラフ間の距離学習2018

    • Author(s)
      吉田知貴, 竹内一郎, 烏山昌幸
    • Organizer
      , 情報論的学習理論と機械学習研究会
    • Related Report
      2018 Annual Research Report
  • [Presentation] Factor Analysis on a Graph2018

    • Author(s)
      M. Karasuyama and H. Mamitsuka
    • Organizer
      International Conference on Artificial Intelligence and Statistics
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Presentation] マージン最大化距離学習におけるセーフスクリーニング2017

    • Author(s)
      吉田知貴, 竹内一郎, 烏山昌幸
    • Organizer
      情報論的学習理論と機械学習研究会
    • Related Report
      2017 Annual Research Report
  • [Presentation] 系列データからのクラス特異的代表パターン選出: 分類モデルとMorse Complexによるアプローチ2017

    • Author(s)
      烏山昌幸, 竹内一郎
    • Organizer
      情報論的学習理論と機械学習研究会
    • Related Report
      2017 Annual Research Report

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Published: 2017-04-28   Modified: 2022-01-27  

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