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Analysis of complex and high dimensional data via sparse regularization techniques

Research Project

Project/Area Number 15K15946
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeMulti-year Fund
Research Field Statistical science
Research InstitutionTokyo Institute of Technology

Principal Investigator

Katayama Shota  東京工業大学, 工学院, 助教 (50742459)

Project Period (FY) 2015-04-01 – 2018-03-31
Project Status Completed (Fiscal Year 2017)
Budget Amount *help
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Keywords高次元データ / スパース正則化 / ロバスト推測 / グラフィカルモデル / スパース推定 / ロバスト推定
Outline of Final Research Achievements

Some researches on complex and high dimensional data have been conducted via sparse regularization techniques. This research particularly focuses on dealing with outliers and exploiting a group structure. On the former case, robust and sparse linear regression analyses have been proposed when responses may be corrupted. An estimation technique of conditional independences among large dimensional variables also has been proposed under cell-wise corruption of data matrix. On the latter case, a simultaneous detection method of both covariates that entirely and partially affect responses has been proposed in the context of stratified linear regression.

Report

(4 results)
  • 2017 Annual Research Report   Final Research Report ( PDF )
  • 2016 Research-status Report
  • 2015 Research-status Report
  • Research Products

    (9 results)

All 2018 2017 2016 2015 Other

All Int'l Joint Research (1 results) Journal Article (2 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 2 results,  Acknowledgement Compliant: 1 results) Presentation (5 results) (of which Int'l Joint Research: 1 results,  Invited: 1 results) Remarks (1 results)

  • [Int'l Joint Research] University of Washington(米国)

    • Related Report
      2017 Annual Research Report
  • [Journal Article] Robust and sparse Gaussian graphical modeling under cell-wise contamination2018

    • Author(s)
      Shota Katayama, Hironori Fujisawa, Mathias Drton
    • Journal Title

      Stat

      Volume: 7 Issue: 1

    • DOI

      10.1002/sta4.181

    • Related Report
      2017 Annual Research Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] Sparse and robust linear regression: an optimization algorithm and its statistical properties2017

    • Author(s)
      Shota Katayama and Hironori Fujisawa
    • Journal Title

      Statistica Sinica

      Volume: 印刷中 Pages: 1243-1264

    • DOI

      10.5705/ss.202015.0179

    • Related Report
      2017 Annual Research Report 2016 Research-status Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Presentation] Adaptive generalized lasso for high-dimensional linear regression model2017

    • Author(s)
      Shota Katayama
    • Organizer
      統計関連学会連合大会
    • Related Report
      2017 Annual Research Report
  • [Presentation] Support recovery of adaptive generalized lasso under high-dimensionality2017

    • Author(s)
      片山翔太
    • Organizer
      平成29年度科学研究費シンポジウム「大規模複雑データの理論と方法論,及び,関連分野への応用」
    • Related Report
      2017 Annual Research Report
  • [Presentation] 外れ値にロバストな非凸スパース正則化回帰2016

    • Author(s)
      片山翔太
    • Organizer
      統計関連学会連合大会
    • Place of Presentation
      金沢大学
    • Year and Date
      2016-09-05
    • Related Report
      2016 Research-status Report
  • [Presentation] Robust non-convex penalized linear regression with algorithmic and statistical convergence2016

    • Author(s)
      片山翔太
    • Organizer
      IMS-APRM 2016
    • Place of Presentation
      The Chinese University of Hong Kong
    • Year and Date
      2016-06-27
    • Related Report
      2015 Research-status Report
    • Int'l Joint Research
  • [Presentation] Robust high-dimensional regression with algorithmic convergence and support recovery2015

    • Author(s)
      片山翔太
    • Organizer
      統計関連学会連合大会
    • Place of Presentation
      岡山大学 津島キャンパス
    • Year and Date
      2015-09-06
    • Related Report
      2015 Research-status Report
    • Invited
  • [Remarks] Shota Katayama's Home Page

    • URL

      http://www.me.titech.ac.jp/~miyalab/katayama/index.html

    • Related Report
      2017 Annual Research Report

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Published: 2015-04-16   Modified: 2022-06-07  

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