Open Access
Issue
ITM Web Conf.
Volume 7, 2016
3rd Annual International Conference on Information Technology and Applications (ITA 2016)
Article Number 02004
Number of page(s) 9
Section Session 2: Signal and Image Processing
DOI https://doi.org/10.1051/itmconf/20160702004
Published online 21 November 2016
  1. J. Wright, A Y. Yang, A. Ganesh, S. S. Sastry, Y. Ma . Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 210–227(2009) [CrossRef] [Google Scholar]
  2. J .Z. Huang, X. L. Huang, D. Metaxas Simultaneous image transformation and sparse representation recovery. In: Proceedings of the 26th IEEE Conference on Computer Vision and Image Recognition. Anchorage United States. 1–8(2008) [Google Scholar]
  3. J. Yang, L. Zhang, Y. Xu, and J.Y. Yang, Beyond sparsity: The role of L1-optimizer in pattern classification, Pattern Recognit,45 1104–1118(2012) [CrossRef] [Google Scholar]
  4. J. Yang, A. F. Frang, J.-Y. Yang, D. Zhang, and Z. Jin, KPCA plus LDA: A complete kernel fisher discriminant framework for feature extraction and recognition, IEEE Trans. Pattern Anal. Mach. Intell. 27(2) 230–244 (2005) [CrossRef] [Google Scholar]
  5. H. Cevikalp, M. Neamtu, and M. Wilkes, Discriminative common vector method with kernels, IEEE Trans. Neural Netw, 17(6) 1550–1565(2006) [CrossRef] [Google Scholar]
  6. L. Zhang, M. Yang and X. C. Feng, Sparse representation or collaborative representation: Which helps face recognition? In Proc. IEEE Int. Conf. Comput. Vis., Nov. 3 7(2011) [Google Scholar]
  7. R. He, W.-S. Zheng and B.G. Hu, Maximum Correntropy criterion for robust face recognition, IEEE Trans. Pattern Anal. Mach. Intell. 33, 1561–1576(2011) [CrossRef] [Google Scholar]
  8. R. He, B.-G. Hu, and X. T. Yuan, Robust discriminant analysis based on non-parametric maximum entropy, In Proc. Asian Conf. Mach. Learn, 120–134(2009) [Google Scholar]
  9. Z. Zhang, H. Zha. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J. Scientific Computing, 26 313–338(2004) [Google Scholar]
  10. Z. Y. Zhang, J. Wang, H. Y. Zha. Adaptive manifold learning IEEE. Trans. Pattern .Anal. Mach. Int. 32 253–265 (2012) [CrossRef] [Google Scholar]
  11. Z. Lai, M. Wan, Z. Jin, and J. Yang, Sparse two-dimensional local discriminant projections for feature extraction, Neuro.Computing, 4 629–637,(2011) [Google Scholar]
  12. M. Yang, L. Zhang, Gabor feature based spare representation for face recognition with Gabor occlusion dictionary, In ECCV,448–461 (2010) [Google Scholar]
  13. N. Zhang, J. K Yang. Nearest neighbor based local sparse representation classifier, In: Proceedings of the 2010 Chinese Conference on Pattern Recognition. Chongqing, China: CCPR, 400–404 (2010) [Google Scholar]
  14. R. Tibshiraniomput, Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Society: Series B, 73(3) 273–282(2011) [CrossRef] [MathSciNet] [Google Scholar]
  15. J Zhang, R. Jin, Y. M. Yang, Y. M. Hauptmann, A. G . Modified, Logistic regression: an approximation to SVM and its applications in large-scale text categorization, In: Proceedings of the 20th International Conference on Machine Learning. Washington, United states: ICML, 888–895(2003) [Google Scholar]
  16. Y. FU, T.S. Huang. Graph embedded analysis for head pose estimation, IEEE International Conf. on Automatic Face and Gesture Recognition. (2006) [Google Scholar]
  17. H .T. Chen, H. W. Chang, and T. L. Liu, Local discriminant embedding and its variants, Proc. Conf. Comput.Vis. Pattern Recognit. 846–853(2005) [Google Scholar]
  18. S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, and S. Lin, Graph embedding and extension: A general framework for dimensionality reduction, IEEE Trans. Pattern Anal. Mach. Intell. 29, 40–51(2007) [CrossRef] [Google Scholar]
  19. D. Cai, X. He, Y. Hu, J. Han and T. Huang, Learning a spatially smooth subspace for face recognition, Proc. Comput.Soc. Conf. Comput.Vis. Pattern Recognit., 1–7 (2007) [Google Scholar]
  20. Z. Fan, Y. Xu, and D. Zhang, Local linear discriminant analysis framework using sample neighbors, IEEE Trans. Neural Netw, 22, 1119–1132 (2011) [CrossRef] [Google Scholar]

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