Statistical Machine Learning with Heterogeneous Auxiliary Information
| Project/Area Number |
25870322
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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
Intelligent informatics
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| Research Institution | Gifu University |
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Principal Investigator |
SHIGA Motoki 岐阜大学, 工学部, 助教 (20437263)
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| Project Period (FY) |
2013-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2013: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 行列分解 / クラスタ解析 / スパース正則化 / 変分ベイズ学習 / 機械学習 / 非負値行列分解 / 特徴選択 / 半教師あり学習 / クラスタリング |
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| Outline of Final Research Achievements |
Recent developments of measuring engineering enable us to simultaneously monitor multiple variables and then the common size of datasets has been increasing. Thus finding essential simple rules hidden in such huge datasets becomes important. This research project has developed efficient clustering and matrix/tensor factorization methods by combining auxiliary information provided from databases and experts. Among a lot of data structures of auxiliary information, this project focused on auxiliary group and network structures. These results were also applied to data analysis on genome science, medical research, and material science.
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Report
(4 results)
Research Products
(9 results)
