Boosting performance for 2D linear discriminant analysis via regression
Two dimensional linear discriminant analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2008
|
| Online Access: | http://hdl.handle.net/20.500.11937/3201 |
| _version_ | 1848744166621708288 |
|---|---|
| author | Nguyen, Nam Liu, Wan-Quan Venkatesh, Svetha |
| author2 | Not known |
| author_facet | Not known Nguyen, Nam Liu, Wan-Quan Venkatesh, Svetha |
| author_sort | Nguyen, Nam |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Two dimensional linear discriminant analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms. |
| first_indexed | 2025-11-14T05:57:09Z |
| format | Conference Paper |
| id | curtin-20.500.11937-3201 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T05:57:09Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-32012018-03-29T09:05:21Z Boosting performance for 2D linear discriminant analysis via regression Nguyen, Nam Liu, Wan-Quan Venkatesh, Svetha Not known Two dimensional linear discriminant analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms. 2008 Conference Paper http://hdl.handle.net/20.500.11937/3201 10.1109/ICPR.2008.4761898 IEEE restricted |
| spellingShingle | Nguyen, Nam Liu, Wan-Quan Venkatesh, Svetha Boosting performance for 2D linear discriminant analysis via regression |
| title | Boosting performance for 2D linear discriminant analysis via regression |
| title_full | Boosting performance for 2D linear discriminant analysis via regression |
| title_fullStr | Boosting performance for 2D linear discriminant analysis via regression |
| title_full_unstemmed | Boosting performance for 2D linear discriminant analysis via regression |
| title_short | Boosting performance for 2D linear discriminant analysis via regression |
| title_sort | boosting performance for 2d linear discriminant analysis via regression |
| url | http://hdl.handle.net/20.500.11937/3201 |