Variable Selection for Small Sample and High Dimension Case by Semi-supervised Learning and Its Application to Super-Resolution
| Project/Area Number |
25870503
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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
Perceptual information processing
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| Research Institution | Kyushu University |
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Principal Investigator |
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| Project Period (FY) |
2013-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 半教師付き学習 / MDL原理 / 超解像 / Barron and Cover理論 / スパースコーディング / 最小記述長原理 / 共変量シフト / リスクバウンド / 重み付き尤度法 / lasso / リスク上界 / MDL / 影響関数 / 最有効推定量 / セミパラメトリック理論 |
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| Outline of Final Research Achievements |
The main important result of this study is that we provided a way to extend Barron and Cover’s theory to supervised learning without any significant lack of its virtues, which had been considered to be difficult. Our extension leads to a risk estimator of supervised learning without conventional assumptions like boundedness of random variables and/or asymptotic assumption. By our method, we succeeded in deriving a new risk bound of the most famous compressed sensing algorithm (lasso). We also extended these results to semi-supervised learning and sparse coding. Furthermore, by implementing semi-supervised sparse coding, we construct a new semi-supervised super-resolution algorithm. We show that the accuracy of super-resolution can be improved by semi-supervised super-resolution by numerical experiments though its extent strongly depends on input images.
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Report
(5 results)
Research Products
(13 results)
