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A machine learning based approach to analysing latent substructure of graph data

Research Project

Project/Area Number 26730120
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeMulti-year Fund
Research Field Intelligent informatics
Research InstitutionNagoya Institute of Technology (2015-2016)
Kyoto University (2014)

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) 2014-04-01 – 2017-03-31
Project Status Completed (Fiscal Year 2016)
Budget Amount *help
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2016: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Keywords機械学習 / グラフ / 多様体学習 / 半教師学習 / 半教師付き学習 / 多様体 / バイオインフォマティクス
Outline of Final Research Achievements

A variety of data can be represented as a graph. For statistical data analysis based on graphs, it is important to consider setting appropriate parameters of graphs and extracting informative structure. This study focuses on a methodological study of ``machine learning'' based on graph data. In particular, the label estimation problem on a graph and the important subgraph identification problem have been considered. For example, accurate label estimation methods and scalable subgraph identification methods are required by biological data analysis.

Report

(4 results)
  • 2016 Annual Research Report   Final Research Report ( PDF )
  • 2015 Research-status Report
  • 2014 Research-status Report
  • Research Products

    (9 results)

All 2017 2016 2015 2014

All Journal Article (1 results) (of which Peer Reviewed: 1 results,  Open Access: 1 results,  Acknowledgement Compliant: 1 results) Presentation (7 results) (of which Int'l Joint Research: 1 results,  Invited: 4 results) Book (1 results)

  • [Journal Article] Adaptive Edge Weighting for Graph-Based Learning Algorithms2017

    • Author(s)
      M. Karasuyama, and H. Mamitsuka,
    • Journal Title

      Machine Learning

      Volume: 106 Issue: 2 Pages: 307-335

    • DOI

      10.1007/s10994-016-5607-3

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed / Open Access / Acknowledgement Compliant
  • [Presentation] 多様体学習に基づく特徴クラスタリング2016

    • Author(s)
      烏山昌幸
    • Organizer
      応用数理学会2016年会
    • Place of Presentation
      九州
    • Year and Date
      2016-09-12
    • Related Report
      2016 Annual Research Report
  • [Presentation] 機械学習による粒界データ解析2016

    • Author(s)
      烏山昌幸
    • Organizer
      応用数理学会2016年会
    • Place of Presentation
      九州
    • Year and Date
      2016-09-12
    • Related Report
      2016 Annual Research Report
    • Invited
  • [Presentation] Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining2016

    • Author(s)
      K. Nakagawa, S. Suzumura, M. Karasuyama, K. Tsuda, and I. Takeuchi
    • Organizer
      The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    • Place of Presentation
      USA
    • Year and Date
      2016-08-13
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] 複数多様体の同時推定に基づく特徴クラスタリング2015

    • Author(s)
      烏山昌幸
    • Organizer
      情報論的学習理論と機械学習
    • Place of Presentation
      筑波
    • Year and Date
      2015-11-27
    • Related Report
      2015 Research-status Report
  • [Presentation] 機械学習によるグラフデータ解析2015

    • Author(s)
      烏山昌幸
    • Organizer
      日本生体医工学会
    • Place of Presentation
      名古屋
    • Year and Date
      2015-05-09
    • Related Report
      2015 Research-status Report
    • Invited
  • [Presentation] Learning Kernel-based Feature Representation for Gene Essentiality Prediction2014

    • Author(s)
      烏山昌幸
    • Organizer
      RECOMB/ISCB Conference on Regulatory and Genomics with DREAM Challenges and Cytoscape Wrokshops 2014
    • Place of Presentation
      アメリカ
    • Year and Date
      2014-11-11
    • Related Report
      2014 Research-status Report
    • Invited
  • [Presentation] Manifold-based Similarity Adaptation for Label Propagation2014

    • Author(s)
      烏山昌幸
    • Organizer
      第17回 画像の認識・理解シンポジウム
    • Place of Presentation
      岡山
    • Year and Date
      2014-07-29
    • Related Report
      2014 Research-status Report
    • Invited
  • [Book] 統計的学習の基礎 -データマイニング・推論・予測-2014

    • Author(s)
      杉山 将, 井手 剛, 神嶌 敏弘, 栗田 多喜夫, 前田 英作監訳, 井尻 善久, 井手 剛, 岩田 具治, 金森 敬文, 兼村 厚範, 烏山 昌幸, 河原 吉伸, 木村 昭悟, 小西 嘉典, 酒井 智弥, 鈴木 大慈, 竹内 一郎, 玉木 徹, 出口 大輔, 冨岡 亮太, 波部 斉, 前田 新一, 持橋 大地, 山田 誠
    • Total Pages
      888
    • Publisher
      共立出版
    • Related Report
      2014 Research-status Report

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Published: 2014-04-04   Modified: 2018-03-22  

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