Ridge regression for two dimensional locality preserving projection
Contribuinte(s) |
[Unknown] |
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Data(s) |
01/01/2008
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Resumo |
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 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 RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30044589/venkatesh-ridgeregression-2008.pdf http://www.icpr2008.org/ http://dx.doi.org/10.1109/ICPR.2008.4761132 |
Direitos |
2008, IEEE |
Palavras-Chave | #computational costs #face data #face recognition algorithms #FERET database #locality preserving projections #novel algorithm #ridge regression |
Tipo |
Conference Paper |