Multivariate statistical modeling for information integration via sparse learning
| Project/Area Number |
15K15947
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Statistical science
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| Research Institution | The University of Electro-Communications |
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Principal Investigator |
KAWANO SHUICHI 電気通信大学, 大学院情報理工学研究科, 准教授 (50611448)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 機械学習 / スパース学習 / 主成分回帰 / 一般化線形モデル / オンライン学習 / 順序カテゴリカルデータ / 情報量規準 / ベイズモデリング / 主成分分析 / 回帰分析 / ベイジアンアプローチ |
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| Outline of Final Research Achievements |
We developed statistical methods for information integration that can learn a structure between data and provide a predictive model based on the given structure simultaneously. In particular, we presented a one-stage procedure for principal component regression and developed various data analysis techniques based on sparse modeling and online learning. We also proposed a procedure for selecting a value of tuning parameters in sparse modeling from the viewpoint of information theory and Bayesian approach. We applied the proposed methods into various datasets including mouse consomic strain data.
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Report
(4 results)
Research Products
(30 results)
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[Presentation] スパース推定と統計解析2015
Author(s)
川野秀一
Organizer
2015年度統計関連学会連合大会
Place of Presentation
岡山大学（岡山県・岡山市）
Year and Date
2015-09-06
Related Report
Invited
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