Nonlinear multivariate analysis based on statistical machine learning theory
| Project/Area Number |
24700280
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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 (2014)
Osaka Prefecture University (2012-2013) |
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Principal Investigator |
KAWANO SHUICHI 電気通信大学, その他の研究科, 准教授 (50611448)
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2014: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2012: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 半教師あり学習 / 関数データ解析 / モデル評価基準 / スパース推定 / 正則化法 / 異分布性 / 機械学習 / 判別分析 / スパース学習 / ベイズ理論 / 高次元データ |
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| Outline of Final Research Achievements |
We developed nonlinear statistical methods to extract useful information from high-dimensional diverse data. In particular, we proposed semi-supervised methods that can treat functional data or labeled data and unlabeled data from different sampling distributions, and developed a series of procedures for evaluating and predicting statistical models based on sparse estimation. We applied the proposed methods to datasets in the various fields of research including life science.
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Report
(4 results)
Research Products
(23 results)
