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Ridge Regression for Two Dimensional Locality Preserving Projection

9 years 4 months ago
Ridge Regression for Two Dimensional Locality Preserving Projection
Two Dimensional Locality Preserving Projection (2DLPP) 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 (RR2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP 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 RR2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.
Nam Thanh Nguyen, Wanquan Liu, Svetha Venkatesh
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICPR
Authors Nam Thanh Nguyen, Wanquan Liu, Svetha Venkatesh
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