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).

1. Balakrishnama.S.Ganapathiraju, “Linear Discriminant Analysis- A brief Tutorial”, Institute for signal and Information, MS, 1998.
2. Daoqiang Zhang, Zhi_Hua Zhou, Songcan Chen, Diagonal Principal Component Analysis for face recognition, Pattern recognition 2006, 39(1), pp 140-142.
3. Dewan Hu, Guiyu feng, Zongtan Zhou, Two Dimensional locality Preserving projections with its application to palm print recognition , Pattern Recognition 2007, Vol 40(10), pp 339-342.
4. He.X and Niyogi.P, “Locality Preserving Projections”, Advances in Neural Information Processing Systems, 16, 2003.
5. He.X.F, S.YAn, Y.Hu, P.Niyogi and H.J.Zhang, Face recognition using Laplacianfaces, IEEE Trans. Pattern Analysis and Machine Intelligence, 2005, Vol 2793, pp 328-340.
6. Hyvarinen A, “Survey on Independent Component Analysis”, Neural Computing Surveys 1999, Vol 2: 94-128
7. Jolliffe.I.T, “Principal Component Analysis” Springer Verlag, NY, 1986.
8. Roweis.S.T and L.K.Saul, “Nonlinear dimensionality reduction by locally linear embedding”, Science 2000, Vol.290, pp 2323-2326
9. Scholkopf.B, Smola.A, and Muller.K.R, “Kernel PCA”, Advances in Kernel methods, Support Vector Learning, MIT Press, 1999, pp.327-352.
10. Sibao Chen, Haifeng Zhao, Min Kong, and Bin Luo, 2D-LPP: a two-dimensional extension of locality preserving projections, Journal of Neuro-computing, 2007, Vol.70 (4-6), pp 912-921.
11. Veerabhadrappa, Lalitha Rangarajan, B.H.Shekar, Alternate 2DLPP: A new dimensionality reduction technique for clustering” Proceedings of National Conference on Recent Trends in Information and Communication Technology (RTICT2008) Tamilnadu, India,2008, pp 240-244.
12. Veerabhadrappa, Lalitha Rangarajan, B.H.Shekar, (2D)2LPP: A new dimensionality reduction technique with application to face/object representation and recognition, International Journal of Systemics, Cybernetics and Informatics, April 2009, pp 17-22.
13. Xin Geng, De-Chuan Zhan, and Zhi-Hua Zhou, “Supervised Nonlinear Dimensionality Reduction for Visualization and Classification”, IEEE Transactions on Systems, Man and Cybernetics, 2005, Vol 35(6), pp 1098-1107.
14. Yang.J, D. Zhang, A.F. Frangi, and J.Yang, Two-Dimensional PCA: A new approach to appearance based face representation and recognition, IEEE Trans. Pattern Analysis and Machine Intelligence, 2004, Vol.26(1), pp 131-137.

Locality Preserving Projection (LPP) Two-dimensional LPP Principal Component Analysis (PCA) Dimensionality Reduction Diagonal image face recognition