Regularized locality preserving discriminant embedding for face recognition

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Regularized locality preserving discriminant embedding for face recognition

For face recognition, graph embedding techniques attempt to produce a high data locality projection for better recognition performance. However, estimation of population data locality could be severely biased due to small number of training samples. The biased estimation triggers overfitting problem...

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Bibliographic Details
Main Authors:	Pang, Ying Han, Teoh, Andrew Beng Jin, Abas, Fazly Salleh
Format:	Article
Language:	English
Published:	Elsevier Science 2012
Subjects:	QA75.5-76.95 Electronic computers. Computer science
Online Access:	http://shdl.mmu.edu.my/3387/ http://shdl.mmu.edu.my/3387/1/Regularized%20Locality%20Preserving%20Discriminant%20Embedding%20for%20Face%20Recognition.pdf

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