Probabilistic generative models and learning -application to realistic data and brain measurements-
| Project/Area Number |
20700220
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | National Institute of Information and Communications Technology |
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Principal Investigator |
TERAZONO Yasushi National Institute of Information and Communications Technology, 大学院・新領域創成科学研究科, 特任研究員 (90435785)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2010: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2009: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2008: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 確率 / 生成モデル / 脳活動 / 隠れ状態 / MEG / 逆問題 / 11-ノルム / 疎 / スパース / ブロックスパース / ベクトル場 / 2次錐計画問題 / l1-ノルム / 学習 / 情報量 |
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| Research Abstract |
Problems of estimating sparse source vector fields from the measured data of them were addressed by assuming a generative model. These fields can be regarded as block-sparse vectors, that have larger values at smaller points and whose elements have groupwise (blockwise) structure. (1) A source estimation method robust against forward calculation errors was proposed, and (2) A reconstruction principle of point source in linear inverse problems was proved.
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Report
(4 results)
Research Products
(5 results)
