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Development of statistical data analysis framework for navigation and human mobility data analysis

Planned Research

Project AreaSystems Science of Bio-navigation
Project/Area Number 16H06538
Research Category

Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)

Allocation TypeSingle-year Grants
Review Section Complex systems
Research InstitutionNagoya Institute of Technology

Principal Investigator

Ichiro Takeuchi  名古屋工業大学, 工学(系)研究科(研究院), 教授 (40335146)

Co-Investigator(Kenkyū-buntansha) 打矢 隆弘  名古屋工業大学, 工学(系)研究科(研究院), 准教授 (10375157)
梶岡 慎輔  名古屋工業大学, 工学(系)研究科(研究院), 助教 (40609517)
烏山 昌幸  名古屋工業大学, 工学(系)研究科(研究院), 准教授 (40628640)
Project Period (FY) 2016-06-30 – 2021-03-31
Project Status Completed (Fiscal Year 2020)
Budget Amount *help
¥84,500,000 (Direct Cost: ¥65,000,000、Indirect Cost: ¥19,500,000)
Fiscal Year 2020: ¥16,640,000 (Direct Cost: ¥12,800,000、Indirect Cost: ¥3,840,000)
Fiscal Year 2019: ¥16,640,000 (Direct Cost: ¥12,800,000、Indirect Cost: ¥3,840,000)
Fiscal Year 2018: ¥17,550,000 (Direct Cost: ¥13,500,000、Indirect Cost: ¥4,050,000)
Fiscal Year 2017: ¥17,550,000 (Direct Cost: ¥13,500,000、Indirect Cost: ¥4,050,000)
Fiscal Year 2016: ¥16,120,000 (Direct Cost: ¥12,400,000、Indirect Cost: ¥3,720,000)
Keywords機械学習 / 軌跡マイニング / 選択的推論 / 系列マイニング / 動物行動学 / 変化点検出 / 系列データ分析 / 統計推論 / 動物行動 / データマイニング / 時系列データ / バイオロギング / 人工知能 / 統計科学 / 時系列解析 / ヒト行動認識
Outline of Final Research Achievements

In this study, we established a statistical data analysis methods that can be used to analyze various moving behaviors of various animals (including humans). In this study, we introduced an approach called selective inference and to enable statistical inference of the results of animal behavior data analysis. In particular, we developed data analysis methods with statistical reliability guarantee in the tasks of extracting partial trajectories that differ among groups and extracting change points from movement trajectories. The developed data analysis methods were applied to various trajectory analysis of various animal species including humans, and its usefulness was demonstrated.

Academic Significance and Societal Importance of the Research Achievements

計測技術の発展により,車,ヒト,動物などの移動行動計測が可能となった.膨大な移動行動データを分析して知識を抽出する際には,統計的選択バイアスが生じるため,これまでは正しい信頼性評価が困難であった.本研究では,移動行動分析分野で初めて選択的推論と呼ばれる選択バイアス補正法を活用し,移動行動データから信頼性の高い知識を得る枠組を初めて開発し,その有効性を実証した.

Report

(6 results)
  • 2020 Annual Research Report   Final Research Report ( PDF )
  • 2019 Annual Research Report
  • 2018 Annual Research Report
  • 2017 Annual Research Report
  • 2016 Annual Research Report
  • Research Products

    (53 results)

All 2021 2020 2019 2018 2017 2016

All Journal Article (21 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 20 results,  Open Access: 10 results,  Acknowledgement Compliant: 2 results) Presentation (32 results) (of which Int'l Joint Research: 12 results,  Invited: 11 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 Issue: 1 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] Selective inference for high-order interaction features selected in a stepwise manner2021

    • Author(s)
      Shinya Suzumura, Kazuya Nakagawa, Yuta Umezu, Koji Tsuda, Ichiro Takeuchi
    • Journal Title

      IPSJ Transactions on Bioinformatics

      Volume: 14 Issue: 0 Pages: 1-11

    • DOI

      10.2197/ipsjtbio.14.1

    • NAID

      130007985966

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty2020

    • Author(s)
      Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi.
    • Journal Title

      IEEE Access

      Volume: 8 Pages: 203982-203993

    • DOI

      10.1109/access.2020.3036863

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming.2020

    • Author(s)
      Vo Nguyen Le Duy, Hiroki Toda, Ryota Sugiyama, Ichiro Takeuchi
    • Journal Title

      Proceedings of 34th Conference on Neural Information Processing Systems (NeurIPS2020)

      Volume: NA

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions2020

    • Author(s)
      Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Ichiro Takeuchi
    • Journal Title

      Neural Computation

      Volume: 32 Issue: 12 Pages: 2486-2531

    • DOI

      10.1162/neco_a_01332

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation2020

    • Author(s)
      Inatsu Yu、Karasuyama Masayuki、Inoue Keiichi、Kandori Hideki、Takeuchi Ichiro
    • Journal Title

      Neural Computation

      Volume: 32 Issue: 10 Pages: 1998-2031

    • DOI

      10.1162/neco_a_01310

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives2020

    • Author(s)
      Inatsu Yu、Sugita Daisuke、Toyoura Kazuaki、Takeuchi Ichiro
    • Journal Title

      Neural Computation

      Volume: 32 Issue: 10 Pages: 2032-2068

    • DOI

      10.1162/neco_a_01307

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Computing Valid P-Values for Image Segmentation by Selective Inference2020

    • Author(s)
      Tanizaki Kosuke、Hashimoto Noriaki、Inatsu Yu、Hontani Hidekata、Takeuchi Ichiro
    • Journal Title

      Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 (CVPR2020)

      Volume: - Pages: 9550-9559

    • DOI

      10.1109/cvpr42600.2020.00957

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images2020

    • Author(s)
      Hashimoto Noriaki、Fukushima Daisuke、Koga Ryoichi、Takagi Yusuke、Ko Kaho、Kohno Kei、Nakaguro Masato、Nakamura Shigeo、Hontani Hidekata、Takeuchi Ichiro
    • Journal Title

      Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 (CVPR2020)

      Volume: - Pages: 3851-3860

    • DOI

      10.1109/cvpr42600.2020.00391

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Computing Full Conformal Prediction Set with Approximate Homotopy2019

    • Author(s)
      Ndiaye E., Takeuchi I.
    • Journal Title

      Proceedings of 33rd Conference on Neural Information Processing Systems (NeurIPS2019)

      Volume: NA

    • 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 Issue: 12 Pages: 2432-2491

    • DOI

      10.1162/neco_a_01240

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction2019

    • Author(s)
      Le Duy Vo Nguyen、Sakuma Takuto、Ishiyama Taiju、Toda Hiroki、Arai Kazuya、Karasuyama Masayuki、Okubo Yuta、Sunaga Masayuki、Tabei Yasuo、Takeuchi Ichiro
    • Journal Title

      Proceedings of International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019)

      Volume: - Pages: 548-551

    • DOI

      10.1145/3347146.3359379

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed
  • [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 Issue: 3-4 Pages: 134-152

    • DOI

      10.1080/01691864.2019.1571438

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Can AI predict animal movements? Filling gaps in animal trajectories using inverse reinforcement learning2018

    • Author(s)
      Tsubasa Hirakawa, Takayoshi Yamashita, Toru Tamaki, Hironobu Fujiyoshi, Yuta Umezu, Ichiro Takeuchi, Sakiko Matsumoto, Ken Yoda
    • Journal Title

      Ecosphere

      Volume: 9 Issue: 10 Pages: 1-24

    • DOI

      10.1002/ecs2.2447

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Efficiently Monitoring Small Data Modification Effect for Large-Scale Learning in Changing Environment2018

    • Author(s)
      H. Hanada,A. Shibagaki,J. Sakuma,I. Takeuchi
    • Journal Title

      Proceedings of The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI2018)

      Volume: NA Pages: 1314-1321

    • Related Report
      2017 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Selective Inference for sparse high-order interaction models2017

    • Author(s)
      S. Suzumura,Y. U. Umezu,K. Tsuda,I. Takeuchi
    • Journal Title

      Proceedings of the 34th International Conference on Machine Learning(ICML2017)

      Volume: NA

    • Related Report
      2017 Annual Research Report
    • Peer Reviewed
  • [Journal Article] 高次交互作用モデリングのための機械学習アルゴリズム日本ロボット学会誌2017

    • Author(s)
      竹内一郎,中川和也,津田宏治
    • Journal Title

      35(3)

      Volume: NA Pages: 215-220

    • Related Report
      2017 Annual Research Report
  • [Journal Article] Homotopy continuation approaches for robust SV classification and regression2016

    • Author(s)
      S. Suzumura, K. Ogawa, M. Karasuyama, M. Sugiyama, I. Takeuchi
    • Journal Title

      Machine Learning

      Volume: - Issue: 7 Pages: 1-30

    • DOI

      10.1007/s10994-017-5627-7

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed / Open Access / Acknowledgement Compliant
  • [Journal Article] Simultaneous safe screening of features and samples in doubly sparse modeling2016

    • Author(s)
      A. Shibagaki M. Karasuyama K. Hatano I. Takeuchi
    • Journal Title

      Proceedings of the 33rd International Conference on Machine Learning

      Volume: 48 Pages: 1577-1586

    • NAID

      40020907513

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining2016

    • Author(s)
      Nakagawa K., Suzumura S., Karasuyama M., Tsuda, K.,Takeuchi I.
    • Journal Title

      Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

      Volume: NA Pages: 1785-1794

    • DOI

      10.1145/2939672.2939844

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed / Open Access / Acknowledgement Compliant
  • [Journal Article] Secure approximation guarantee for cryptographically private empirical risk minimization2016

    • Author(s)
      T. Takada H. Hanada Y. Yamada J. Sakuma I. Takeuchi
    • Journal Title

      Proceedings of the 8th Asian Conference on Machine Learning

      Volume: 63 Pages: 126-141

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed
  • [Presentation] パラメトリック計画法による選択的推論とその応用2020

    • Author(s)
      竹内一郎
    • Organizer
      電子情報通信学会IBISML研究会
    • Related Report
      2020 Annual Research Report
    • Invited
  • [Presentation] Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming.2020

    • Author(s)
      Vo Nguyen Le Duy, Hiroki Toda, Ryota Sugiyama, Ichiro Takeuchi
    • Organizer
      34th Conference on Neural Information Processing Systems (NeurIPS2020)
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Computing Valid P-values for Image Segmentation by Selective Inference.2020

    • Author(s)
      Kosuke Tanizaki, Noriaki Hashimoto, Yu Inatsu, Hidekata Hontani, Ichiro Takeuchi
    • Organizer
      IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 (CVPR2020)
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Non-annotated Histopathological Images2020

    • Author(s)
      Noriaki Hashimoto, Daisuke Fukushima, Ryoichi Koga, Yusuke Takagi, Kaho Ko, Kei Kohno, Masato Nakaguro, Shigeo Nakamura, Hidekata Hontani, Ichiro Takeuchi
    • Organizer
      IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 (CVPR2020)
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research
  • [Presentation] データ駆動型人工知能による医学生物学研究のとりくみ2019

    • Author(s)
      竹内一郎
    • Organizer
      NIHS特別講演会
    • Related Report
      2019 Annual Research Report
    • Invited
  • [Presentation] Selective Inferenceの理論と応用2019

    • Author(s)
      竹内一郎
    • Organizer
      統計関連学会連合大会チュートリアル
    • Related Report
      2019 Annual Research Report
    • Invited
  • [Presentation] 生物,医療,材料分野におけるシミュレーション科学とデータ科学の融合2019

    • Author(s)
      竹内一郎
    • Organizer
      電子情報通信学会エレクトロニクスシミュレーション研究会
    • Related Report
      2019 Annual Research Report
    • Invited
  • [Presentation] データ駆動型科学のための統計的推論法:Post-Selection Inference / Selective Inference2018

    • Author(s)
      竹内一郎
    • Organizer
      電子情報通信学会IBISML研究会チュートリアル
    • Related Report
      2018 Annual Research Report
    • Invited
  • [Presentation] Selective Inference for Unsupervised Learning and Its Application to Heterogeneous Biomedical Data Analysis2018

    • Author(s)
      Ichiro Takeuchi
    • Organizer
      The 10th International Conference on ICT Convergence
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research / Invited
  • [Presentation] 動的計画法を用いた系列セグメンテーションにおけるSelective Inference2018

    • Author(s)
      戸田博己,梅津佑太,佐久間拓人,竹内一郎
    • Organizer
      第21回情報論的学習理論ワークショップ (IBIS2018)
    • Related Report
      2018 Annual Research Report
  • [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
      第21回情報論的学習理論ワークショップ (IBIS2018)
    • Related Report
      2018 Annual Research Report
  • [Presentation] Efficiently Monitoring Small Data Modification Effect for Large-Scale Learning in Changing Environment2018

    • Author(s)
      Hiroyuki Hanada,Atsushi Shibagaki,Jun Sakuma,Ichiro Takeuchi
    • Organizer
      The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI2018)
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Presentation] セーフパターンプルーニングによるルールベースモデルの学習2018

    • Author(s)
      加藤宏樹,花田博幸,竹内一郎
    • Organizer
      電子情報通信学会 第32回情報論的学習理論と機械学習研究会(IBISML)
    • Related Report
      2017 Annual Research Report
  • [Presentation] Fitting and Testing Sparse High-Order Interaction Models2017

    • Author(s)
      竹内一郎
    • Organizer
      H29年度CREST研究集会「大規模統計モデリングと計算統計 IV」
    • Related Report
      2017 Annual Research Report
    • Invited
  • [Presentation] Fitting and Testing Sparse High-Order Interaction Models2017

    • Author(s)
      I. Takeuchi
    • Organizer
      France / Japan Machine Learning Workshop
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research / Invited
  • [Presentation] スパース高次交互作用モデルの最適化アルゴリズム2017

    • Author(s)
      竹内一郎
    • Organizer
      第29回 RAMPシンポジウム
    • Related Report
      2017 Annual Research Report
    • Invited
  • [Presentation] Selective Inference for Predictive Pattern Mining2017

    • Author(s)
      I. Takeuchi
    • Organizer
      Multiple Comparison Procedures 2017
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Selective Inference for sparse high-order interaction models2017

    • Author(s)
      S. Suzumura,Y. U. Umezu,K. Tsuda,I. Takeuchi
    • Organizer
      The 34th International Conference on Machine Learning (ICML2017)
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Selective inferenceに基づくactive learningの選択バイアス補正2017

    • Author(s)
      稲津佑・竹内一郎
    • Organizer
      電子情報通信学会 第31回情報論的学習理論と機械学習研究会(IBISML)
    • Related Report
      2017 Annual Research Report
  • [Presentation] 多次元系列における変化点検出のためのSelective Inference2017

    • Author(s)
      梅津佑太・竹内一郎
    • Organizer
      電子情報通信学会 第31回情報論的学習理論と機械学習研究会(IBISML)
    • Related Report
      2017 Annual Research Report
  • [Presentation] Simultaneous safe screening of features and samples in doubly sparse modeling2016

    • Author(s)
      A. Shibagaki,M. Karasuyama,K. Hatano,I. Takeuchi
    • Organizer
      The 33rd International Conference on Machine Learning
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining2016

    • Author(s)
      K. Nakagawa,S. Suzumura,M. Karasuyama,K. Tsuda,I. Takeuchi
    • Organizer
      The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Secure approximation guarantee for cryptographically private empirical risk minimization2016

    • Author(s)
      T. Takada,H. Hanada,Y. Yamada,J. Sakuma,I. Takeuchi
    • Organizer
      The 8th Asian Conference on Machine Learning (ACML)
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Safe Feature/Sample Screening and Its Applications to High-order Interaction Modeling and Quick Sensitivity Analysis2016

    • Author(s)
      I. Takeuchi
    • Organizer
      The First Korea-Japan Machine Learning Symposium
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research / Invited
  • [Presentation] スパースモデリングのためのセーフスクリーニングとその応用2016

    • Author(s)
      竹内一郎
    • Organizer
      2016年度統計関連学会連合大会
    • Related Report
      2016 Annual Research Report
    • Invited
  • [Presentation] パターンマイニング問題におけるセーフパターンプルーニングを用いたスパースモデルの学習2016

    • Author(s)
      中川和也,鈴村真矢,烏山昌幸,津田宏治,竹内一郎
    • Organizer
      電子情報通信学会第26回情報論的学習理論研究会
    • Related Report
      2016 Annual Research Report
  • [Presentation] 時系列データの変化点検出におけるSelective Inference2016

    • Author(s)
      梅津佑太,中川和也,井上茂乗,津田宏治,杉山麿人,前川卓也,玉木徹,依田憲,竹内一郎
    • Organizer
      電子情報通信学会第26回情報論的学習理論研究会
    • Related Report
      2016 Annual Research Report
  • [Presentation] 区間データに対する経験損失最小化とそのプライバシー保護への応用2016

    • Author(s)
      花田博幸,高田敏行,柴垣篤志,佐久間淳,竹内一郎
    • Organizer
      電子情報通信学会第27回情報論的学習理論研究会
    • Related Report
      2016 Annual Research Report
  • [Presentation] 金森研太,豊浦和明,中島伸一,世古敦人,烏山昌幸,桑原彰秀,本多淳也,設楽和希,志賀元紀,竹内一郎2016

    • Author(s)
      ガウス過程と動的計画法を用いたプロトン伝導体の伝導度推定
    • Organizer
      電子情報通信学会第27回情報論的学習理論研究会
    • Related Report
      2016 Annual Research Report
  • [Presentation] 高次元分類問題のためのSelective Inference2016

    • Author(s)
      梅津佑太,中川和也,津田宏治,竹内一郎
    • Organizer
      電子情報通信学会第27回情報論的学習理論研究会
    • Related Report
      2016 Annual Research Report
  • [Presentation] 経験損失最小化問題における高速感度分析に関する一提案2016

    • Author(s)
      花田博幸,柴垣篤志,佐久間淳,竹内一郎
    • Organizer
      電子情報通信学会第26回情報論的学習理論研究会
    • Related Report
      2016 Annual Research Report
  • [Presentation] スパースモデルのための特徴と標本の同時セーフスクリーニング2016

    • Author(s)
      柴垣篤志,烏山昌幸,畑埜晃平,竹内一郎
    • Organizer
      電子情報通信学会第25回情報論的学習理論研究会
    • Related Report
      2016 Annual Research Report

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Published: 2016-07-04   Modified: 2022-01-27  

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