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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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/10356/64992 |
| _version_ | 1826114275378200576 |
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| author | Gao, Li |
| author2 | Jiang Xudong |
| author_facet | Jiang Xudong Gao, Li |
| author_sort | Gao, Li |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:36:29Z |
| format | Thesis |
| id | ntu-10356/64992 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:36:29Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/649922023-07-04T15:23:59Z Study of the classification in the subspace of the asymmetric principle component analysis Gao, Li Jiang Xudong School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering 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. Master of Science (Signal Processing) 2015-06-10T03:41:10Z 2015-06-10T03:41:10Z 2014 2014 Thesis http://hdl.handle.net/10356/64992 en 63 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Gao, Li Study of the classification in the subspace of the asymmetric principle component analysis |
| title | Study of the classification in the subspace of the asymmetric principle component analysis |
| title_full | Study of the classification in the subspace of the asymmetric principle component analysis |
| title_fullStr | Study of the classification in the subspace of the asymmetric principle component analysis |
| title_full_unstemmed | Study of the classification in the subspace of the asymmetric principle component analysis |
| title_short | Study of the classification in the subspace of the asymmetric principle component analysis |
| title_sort | study of the classification in the subspace of the asymmetric principle component analysis |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/64992 |
| work_keys_str_mv | AT gaoli studyoftheclassificationinthesubspaceoftheasymmetricprinciplecomponentanalysis |