Learning methods for kernel subspace methods and their applications
| Project/Area Number |
19700153
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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 |
Intelligent informatics
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| Research Institution | The Institute of Physical and Chemical Research |
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Principal Investigator |
WASHIZAWA Yoshikazu The Institute of Physical and Chemical Research, 脳信号処理研究チーム, 研究員 (10419880)
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| Project Period (FY) |
2007 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥540,000)
Fiscal Year 2010: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2009: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2008: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2007: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | 部分空間法 / パターン識別 / 機械学習 / カーネル法 / 特徴抽出 / マルチウェイ解析 / テンソル特徴抽出 / 画像復元 / 脳信号処理 / 脳コンピュータインターフェース / 部分カーネル主成分分析 / カーネル部分空間法 / 制約付き近似問題による特徴抽出 / ブラインド信号抽出 / カーネルトリック / 正則化 / Frobeniusノルム / Traceノルム / Tikhonov正則化 / 最適境界識別器 / 交互最小2乗法 / 独立成分分析 / サポートベクタマシン / 構造リスク最小化 |
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| Research Abstract |
We have extended the subspace methods to feature extraction methods by constrained approximation problems. The subspace methods are sorts of realizations of the constrained approximation framework. The subspace methods have the rank constraint, however, by replacing the constraint to the other constraints, feature extractors that have various properties can be realized. We also have proposed the subset kernel principal component analysis to avoid large calculations in kernelization.
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Report
(6 results)
Research Products
(40 results)
