Robust learning of discriminative projection for multicategory classification on the Stiefel manifold
Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/9720 |
| _version_ | 1848746031263514624 |
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| author | Pham, DucSon Venkatesh, Svetha |
| author2 | Not known |
| author_facet | Not known Pham, DucSon Venkatesh, Svetha |
| author_sort | Pham, DucSon |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small. |
| first_indexed | 2025-11-14T06:26:47Z |
| format | Conference Paper |
| id | curtin-20.500.11937-9720 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:26:47Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-97202018-03-29T09:05:56Z Robust learning of discriminative projection for multicategory classification on the Stiefel manifold Pham, DucSon Venkatesh, Svetha Not known Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small. 2008 Conference Paper http://hdl.handle.net/20.500.11937/9720 10.1109/CVPR.2008.4587407 IEEE restricted |
| spellingShingle | Pham, DucSon Venkatesh, Svetha Robust learning of discriminative projection for multicategory classification on the Stiefel manifold |
| title | Robust learning of discriminative projection for multicategory classification on the Stiefel manifold |
| title_full | Robust learning of discriminative projection for multicategory classification on the Stiefel manifold |
| title_fullStr | Robust learning of discriminative projection for multicategory classification on the Stiefel manifold |
| title_full_unstemmed | Robust learning of discriminative projection for multicategory classification on the Stiefel manifold |
| title_short | Robust learning of discriminative projection for multicategory classification on the Stiefel manifold |
| title_sort | robust learning of discriminative projection for multicategory classification on the stiefel manifold |
| url | http://hdl.handle.net/20.500.11937/9720 |