Enhanced graph-based dimensionality reduction with repulsion Laplaceans

Pattern Recognition

Volumn 42, Issue 11, 2009, Pages 2392-2402

  Kokiopoulou, E ^a   Saad, Y ^b  

^a ETH ZURICH   (Switzerland)

^b University of Minnesota   (United States)

Author keywords

Face recognition; Graph Laplacean; Linear dimensionality reduction; Orthogonal projections; Supervised learning

Indexed keywords

CLASSIFICATION TASKS; DATA POINTS; DIMENSIONALITY REDUCTION; EXPERIMENTAL EVIDENCE; GRAPH LAPLACEAN; GRAPH-BASED; GRAPH-BASED METHODS; HIGH DIMENSIONAL SPACES; LINEAR DIMENSIONALITY REDUCTION; NOVEL METHODOLOGY; ORTHOGONAL PROJECTIONS; PERFORMANCE IMPROVEMENTS; REPULSION FORCE; RESEARCH EFFORTS;

EDUCATION; SUPERVISED LEARNING;

FACE RECOGNITION;

EID: 67649413071     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.04.005     Document Type: Article

References (31)

1. Barnard S.T., Pothen A., and Simon H. A spectral algorithm for envelope reduction of sparse matrices. Numerical Linear Algebra with Applications 2 4 (1995) 317-334
2. Jolliffe I.T. Principal Component Analysis (1986), Springer, New York
3. Webb A. Statistical Pattern Recognition. second ed. (2002), Wiley, New York
4. Belhumeur P., Hespanha J., and Kriegman D. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 7 (1997) 711-720 (Special Issue on Face Recognition)
5. M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in: Proceedings of Conference on Advances in Neural Information Processing Systems (NIPS),2002.
6. Cai D., He X., Han J., and Zhang H.-J. Orthogonal Laplacianfaces for face recognition. IEEE Transactions on Image Processing 15 11 (2006) 3608-3614
7. Fruchterman T.M.J., and Reingold E.M. Graph drawing by force-directed placement. Software-Practice and Experience 21 11 (1991) 1129-1164
8. Golub G.H., and Van Loan C.F. Matrix Computations. third ed. (1996), Johns Hopkins University Press, Baltimore, MD
9. D.B. Graham, N.M. Allinson, Characterizing virtual eigensignatures for general purpose face recognition, in: Face Recognition: From Theory to Applications, vol. 163, 1998, pp. 446-456.
10. He X., and Niyogi P. Locality preserving projections. Proceedings of Conference on Advances in Neural Information Processing Systems (2003)
11. He X., Yan S., Hu Y., Niyogi P., and Zhang H.-J. Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 3 (2005) 328-340
12. Horn R.A., and Johnson C.R. Matrix Analysis (1985), Cambridge University Press, Cambridge
13. E. Kokiopoulou, Y. Saad, Orthogonal neighborhood preserving projections, in: J. Han, et al. (Eds.), IEEE Fifth International Conference on Data Mining (ICDM05), Houston, TX, IEEE, November 27-30, 2005, pp. 234-241.
14. Kokiopoulou E., and Saad Y. Orthogonal neighborhood preserving projections: a projection-based dimensionality reduction technique. IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (2007) 2143-2156
15. Y. Koren, On spectral graph drawing, in: COCOON '03, Lecture Notes in Computer Science, vol. 2697, Springer, Berlin, 2003, pp. 496-508.
16. A.M. Martinez, R. Benavente, The AR face database, Technical Report 24, CVC, 1998.
17. A. Noack, An energy model for visual graph clustering, in: Proceedings of the 11th International Symposium on Graph Drawing (GD 2003), Lecture Notes in Computer Science, vol. 2912, Springer, Berlin, 2004, pp. 425-436.
18. Roweis S., and Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science 290 (2000) 2323-2326
19. F. Samaria, A. Harter, Parameterisation of a stochastic model for human face identification, in: Second IEEE Workshop on Applications of Computer Vision, Sarasota, FL, December 1994.
20. Saul L., and Roweis S. Think globally, fit locally: unsupervised learning of nonlinear manifolds. Journal of Machine Learning Research 4 (2003) 119-155
21. J. Shi, J. Malik, Normalized cuts and image segmentation, in: Proceedings of IEEE International Conference on Computer Vision, 1997, pp. 731-737.
22. L. Spacek, University of essex face database, 2002 〈http://cswww.essex.ac.uk/mv/allfaces/index.html〉.
23. U. von Luxburg, A tutorial on spectral clustering, Technical Report TR-149, Max-Planck für biologische Kybernetik, Tuebingen, Germany, 2006.
24. Zhang W., Xue X., Lu H., and Guo Y.-F. Discriminant neighborhood embedding for classification. Pattern Recognition 39 11 (2006) 2240-2243
25. H.-T. Chen, H.-W. Chang, T.-L. Liu, Local discriminant embedding and its variants, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
26. Oja E. Subspace Methods of Pattern Recognition (1984), Electronic & Electrical Engineering Research Studies Press, Wiley
27. Watanabe S. Pattern Recognition: Human and Mechanical (1985), Wiley, New York
28. Watanabe S. Knowing and Guessing: A Quantitative Study of Inference and Information (1969), Wiley, New York
29. Kohonen T. Self-Organizing Maps (2001), Springer, Berlin
30. Oja E., and Kaski S. Kohonen Maps (1999), Elsevier Science, Amsterdam
31. Shawe-Taylor J., and Cristianini N. Support Vector Machines and Other Kernel-based Learning Methods (2000), Cambridge University Press, Cambridge