Ridge Regression for Two Dimensional Locality Preserving Projection
Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Re...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/31142 |
| _version_ | 1848753293123125248 |
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| 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 Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR-2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2D-LPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP. |
| first_indexed | 2025-11-14T08:22:12Z |
| format | Conference Paper |
| id | curtin-20.500.11937-31142 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:22:12Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-311422018-03-29T09:09:00Z Ridge Regression for Two Dimensional Locality Preserving Projection Nguyen, Nam Liu, Wan-Quan Venkatesh, Svetha Not known Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR-2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2D-LPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP. 2008 Conference Paper http://hdl.handle.net/20.500.11937/31142 10.1109/ICPR.2008.4761132 IEEE restricted |
| spellingShingle | Nguyen, Nam Liu, Wan-Quan Venkatesh, Svetha Ridge Regression for Two Dimensional Locality Preserving Projection |
| title | Ridge Regression for Two Dimensional Locality Preserving Projection |
| title_full | Ridge Regression for Two Dimensional Locality Preserving Projection |
| title_fullStr | Ridge Regression for Two Dimensional Locality Preserving Projection |
| title_full_unstemmed | Ridge Regression for Two Dimensional Locality Preserving Projection |
| title_short | Ridge Regression for Two Dimensional Locality Preserving Projection |
| title_sort | ridge regression for two dimensional locality preserving projection |
| url | http://hdl.handle.net/20.500.11937/31142 |