2010 Fiscal Year Final Research Report
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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| Keywords | 部分空間法 / パターン識別 / 機械学習 / カーネル法 |
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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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Research Products
(12 results)
