Study of the classification in the subspace of the asymmetric principle component analysis
My dissertation mainly studies the process of principal component analysis method which widely used for pattern classification. Besides, it analyses the problems of principal component analysis method when the training data are unbalance. A new method called asymmetric principal component a...
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| 其他作者: | |
| 格式: | Theses and Dissertations |
| 語言: | English |
| 出版: |
2015
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| 主題: | |
| 在線閱讀: | http://hdl.handle.net/10356/64992 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | My dissertation mainly studies the process of principal component analysis method
which widely used for pattern classification. Besides, it analyses the problems of
principal component analysis method when the training data are unbalance. A new
method called asymmetric principal component analysis (APCA) is used to remove
the less reliable dimensions to help boost the classification accuracy.
When dealing with a two-class classification problem, the discriminant analysis in
the APCA subspace is used to adjust the eigenvalues so that we can produce more
discriminative and reliable features for the asymmetric classes training data. We have
compared this approach with other approaches. The experimental results show the
highest accuracy among other approaches. We further find out that the optimal
weight factor of different type of training classes have some relationship with the
distribution of the training data. |
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