Laplacian Discriminant Projection Based on Affinity Propagation

1. Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Lin, S.: Graph Embedding and Extension: A General Framework for Dimensionality Reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(1), 40–51 (2007)

   Article  Google Scholar 

2. Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)

   Article  Google Scholar 

3. He, X., Yan, S., Hu, Y.X., Niyogi, P., Zhang, H.: Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005)

   Article  Google Scholar 

4. Belkin, M., Niyogi, P.: Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Computation 15, 1373–1396 (2003)

   Article  MATH  Google Scholar 

5. Zhao, D., Lin, Z., Xiao, R., Tang, X.: Linear Laplacian Discrimination for Feature Extraction. In: CVPR (2007)

   Google Scholar 

6. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, Boston (1990)

   MATH  Google Scholar 

7. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)

   Article  Google Scholar 

8. Weinberger, K., Blitzer, J., Saul, L.: Distance metric learning for large margin nearest neighbor classification. In: NIPS, pp. 1475–1482 (2006)

   Google Scholar 

9. Nie, F., Xiang, S., Zhang, C.: Neighborhood MinMax Projections. In: IJCAI, pp. 993–998 (2007)

   Google Scholar 

10. Howland, P., Park, H.: Generalizing discriminant analysis using the generalized singular value decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(8), 995–1006 (2004)

    Article  Google Scholar 

11. Liu, C.: Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5), 725–737 (2007)

    Google Scholar 

12. Martinez, A., Zhu, M.: Where are linear feature extraction methods applicable. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(12), 1934–1944 (2006)

    Article  Google Scholar 

13. Sim, T., Baker, S., Bsat, M.: The CMU Pose, illumination, and expression (PIE) database. In: IEEE International Conference of Automatic Face and Gesture Recognition (2002)

    Google Scholar 

14. Wang, X., Tang, X.: Dual-space linear discriminant analysis for face recognition. In: CVPR, pp. 564–569 (2004)

    Google Scholar 

15. Yan, S., Xu, D., Zhang, B., Zhang, H.: Graph embedding: A general framework for dimensionality reduction. In: CVPR (2005)

    Google Scholar 

16. Yang, J., Frangi, A., Yang, J., Zhang, D., Jin, Z.: KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(2), 230–244 (2005)

    Article  Google Scholar 

17. Zheng, Z.L., Yang, J., Zhu, Y.: Face detection and recognition using colour sequential images. Journal of Research and Practice in Information Technology 38(2), 135–149 (2006)

    Google Scholar 

18. Zheng, Z.L., Yang, J.: Supervised Locality Pursuit Embedding for Pattern Classification. Image and Vision Computing 24, 819–826 (2006)

    Article  Google Scholar 

19. Wang, X., Tang, X.: A unified framework for subspace face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1222–1228 (2004)

    Article  MathSciNet  Google Scholar 

20. Wang, X., Tang, X.: Random sampling for subspace face recognition. International Journal of Computer Vision 70(1), 91–104 (2006)

    Article  Google Scholar 

21. Frey, B.J., Dueck, D.: Clustering by Passing Messages Between Data Points. Science 315, 972–994 (2007)

    Article  MathSciNet  Google Scholar 

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