Knowledge extraction from large-scale sequence data by combining pattern mining and sparse modeling
| Project/Area Number |
17H04694
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| Research Category |
Grant-in-Aid for Young Scientists (A)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Nagoya Institute of Technology |
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Principal Investigator |
Karasuyama Masayuki 名古屋工業大学, 工学(系)研究科(研究院), 准教授 (40628640)
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| 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)
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| 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 |
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| 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.
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| 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．
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Report
(5 results)
Research Products
(14 results)
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[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
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Journal Title
IEEE Transactions on Knowledge and Data Engineering
Volume: -
DOI
Related Report
Peer Reviewed
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[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
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Journal Title
Advanced Robotics
Volume: 33
Pages: 134-152
DOI
Related Report
Peer Reviewed / Open Access
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