Diagonal and secondary diagonal locality preserving projection for object recognition

Veerabhadrappa, and Rangarajan, Lalitha (2010) Diagonal and secondary diagonal locality preserving projection for object recognition. NEUROCOMPUTING, 73 (16-18,). pp. 3328-3333.

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Official URL: http://dx.doi.org/10.1016/j.neucom.2010.06.008

Abstract

In this paper, the variants of Two Dimensional Locality Preserving Projection (2DLPP) namely Diagonal Locality Preserving Projection (DiaLPP) and Secondary Diagonal Locality Preserving Projection (SDiaLPP) are proposed as the new dimensionality reduction techniques. 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 COIL object database show that the proposed methods achieves higher recognition rate than 2DLPP and Diagonal Principal Component Analysis(DiaPCA). (C) 2010 Elsevier B.V. All rights reserved.

Item Type: Article
Uncontrolled Keywords: Locality preserving projection (LPP); Two dimensional LPP(2DLPP); Principal component analysis (PCA); Dimensionality reduction; Diagonal principal component analysis( DiaPCA); Diagonal LPP(DiaLPP); Secondary diagonal LPP(SDiaLPP); Object recognition
Subjects: Information, Computer and Applied Sciences > Computer Science
Divisions: PG Campuses > Manasagangotri, Mysore > Computer Science
Depositing User: Users 7 not found.
Date Deposited: 25 Mar 2013 10:40
Last Modified: 04 May 2013 04:11
URI: http://eprints.uni-mysore.ac.in/id/eprint/2510

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