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Diagonal Locality Preserving Projection as Dimensionality Reduction Technique with Application to Face Recognition

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RTIPPR
© 2010 by IJCA Journal
Number 3 - Article 5
Year of Publication: 2010
Authors:
Veerabhadrappa
Lalitha Rangarajan

Veerabhadrappa and Lalitha Rangarajan. Diagonal Locality Preserving Projection as Dimensionality Reduction Technique with Application to Face Recognition. IJCA,Special Issue on RTIPPR (3):135–140, 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {Veerabhadrappa and Lalitha Rangarajan},
	title = {Diagonal Locality Preserving Projection as Dimensionality Reduction Technique with Application to Face Recognition},
	journal = {IJCA,Special Issue on RTIPPR},
	year = {2010},
	number = {3},
	pages = {135--140},
	note = {Published By Foundation of Computer Science}
}

Abstract

In this paper, a new dimensionality reduction technique called Diagonal Locality Preserving Projections (DiaLPP) is proposed. In contrast to Locality Preserving Projection (LPP) and Two Dimensional Locality Preserving Projection (2DLPP), DiaLPP directly seeks the optimal projection vectors from diagonal images without vector transformation. The 2DLPP method seeks optimal projection vectors by using the row information of the image and the Alternate 2DLPP method seeks optimal projection vectors by using the column information of the image, whereas the DiaLPP seeks optimal projection vectors by interlacing both the rows and column information of the images. Experimental results on subset of UMIST and ORL face database shows that the proposed method achieves higher recognition rate than 2DLPP, Alternate 2DLPP and DiaPCA (Diagonal Principal Component Analysis).

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