Y. D. Leeuw and D. Cohen, “Diffusion in sparse networks: linear to semilinear crossover,” Phys. Rev. E 86, 051120 (2012).

[CrossRef]

H. Wang, C. Yuan, W. Hub, and C. Sun, “Supervised class-specific dictionary learning or sparse modeling in action recognition,” Pattern Recogn. 45, 3902–3911 (2012).

[CrossRef]

N. Gu, M. Fan, H. Qiao, and B. Zhang, “Discriminative sparsity preserving projections for semi-supervised dimensionality reduction,” IEEE Signal Process. Lett. 19, 391–394 (2012).

[CrossRef]

Y. Sun, J. Zhao, and Y. Hu, “Supervised sparsity preserving projections for face recognition,” Proc. SPIE 8009, 80092D (2011).

[CrossRef]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50, 072601 (2011).

[CrossRef]

J. Wright, Y. Ma, J. Mairal, G. Spairo, T. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).

[CrossRef]

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving discriminant analysis for single training image face recognition,” Pattern Recogn. Lett. 31, 422–429 (2010).

[CrossRef]

Y. Lu, C. Lu, M. Qi, and S. Wang, “A supervised locality preserving projections based local matching algorithm for face recognition,” Lect. Notes Comput. Sci. 6059, 28–37 (2010).

[CrossRef]

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010).

[CrossRef]

J. Wright, A. Yang, A. Ganesh, S. Shastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).

[CrossRef]

J. Ma and F.-X. L. Dimet, “Deblurring from highly incomplete measurements for remote sensing,” IEEE Trans. Geosci. Remote Sens. 47, 792–802 (2009).

[CrossRef]

G. Feng, D. Hu, and Z. Zhou, “A direct locality preserving projections (DLPP) algorithm for image recognition,” Neural Process. Lett. 27, 247–255 (2008).

E. J. Candès and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25 (2), 21–30 (2008).

[CrossRef]

J. Yang, D. Zhang, and J.-Y. Yang, “Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 650–664 (2007).

[CrossRef]

Z. L. Zhang, F. Yang, W. Tan, J. Jia, and J. Yang, “Gabor feature based face recognition using supervised locality preserving projection,” Signal Process. 87, 2473–2483 (2007).

[CrossRef]

D. Hu, G. Feng, and Z. Zhou, “Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition,” Pattern Recogn. 40, 339–342 (2007).

[CrossRef]

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736–3745 (2006).

[CrossRef]

D. Cai, X. He, and L. Han, “Orthogonal Laplacianfaces for face recognition,” IEEE Trans. Image Process. 15, 3608–3614 (2006).

[CrossRef]

J. Yang, A. F. Frangi, J. Y. Yang, D. Zhang, and Z. Jin, “Kernel PCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and representation,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 230–244 (2005).

[CrossRef]

J. Cheng, Q. Liu, H. Lu, and Y.-W. Chen, “Supervised kernel locality preserving projections for face recognition,” Neurocomputing 67, 443–449 (2005).

[CrossRef]

L. K. Saul and S. T. Roweis, “Think globally, fit locally: unsupervised learning of low dimensional manifolds,” J. Mach. Learn. Res. 4, 119–155 (2003).

[CrossRef]

X. He and P. Niyogi, “Locality preserving projections,” Adv. Neural Inf. Process. Syst. 16, 1–8 (2003).

H. Yu and J. Yang, “A direct LDA algorithm for high-dimensional data with application to face recognition,” Pattern Recogn. 34, 2067–2070 (2001).

[CrossRef]

A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 4–37 (2000).

[CrossRef]

S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290, 2323–2326 (2000).

[CrossRef]

B. Scholkopf, A. Smola, and K. R. Muller, “Nonlinear component analysis as a kernel eigenvalues problem,” Neural Comput. 10, 1299–1319 (1998).

[CrossRef]

P. Belhumeur, J. Hesanha, and D. Kreigman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997).

[CrossRef]

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736–3745 (2006).

[CrossRef]

M. H. Yang, N. Ahuja, and D. Kriegman, “Face recognition using kernel eigenfaces,” in Proceedings of International Conf. on Image Processing, Vancouver, Canada (2000), pp. 1–4.

P. Belhumeur, J. Hesanha, and D. Kreigman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997).

[CrossRef]

M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering,” in Advances in Neural Information Processing Systems 14, Vancouver, Canada (2001), pp. 585–591.

Y. Bengio, J. Palement, and P. Vincent, “Out-of-sample extensions for LLE, isomap, MOS, eigenmaps, and spectral clustering,” in Advances in Neural Information Processing Systems 6, Cambridge, MA (2003), p. 117.

D. Cai, X. He, and L. Han, “Orthogonal Laplacianfaces for face recognition,” IEEE Trans. Image Process. 15, 3608–3614 (2006).

[CrossRef]

D. Cai, X. He, and J. Han, “Semi-supervised discriminant analysis,” in Tenth IEEE International Conf. on Computer Vision (IEEE, 2007), pp. 1–7.

D. Cai, X. He, and K. Zhou, “Locality sensitive discriminant analysis,” in Proc. of International Joint Conf. on Artificial Intelligence, Hyderabad, India (2007), pp. 1–6.

X. He, D. Cai, S. Yan, and H.-J. Zhang, “Neighborhood preserving embedding,” in Tenth IEEE International Conf. on Computer Vision, Beijing, China (2005), pp. 1–8.

E. J. Candès and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25 (2), 21–30 (2008).

[CrossRef]

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving discriminant analysis for single training image face recognition,” Pattern Recogn. Lett. 31, 422–429 (2010).

[CrossRef]

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010).

[CrossRef]

J. Cheng, Q. Liu, H. Lu, and Y.-W. Chen, “Supervised kernel locality preserving projections for face recognition,” Neurocomputing 67, 443–449 (2005).

[CrossRef]

J. Cheng, Q. Liu, H. Lu, and Y.-W. Chen, “Supervised kernel locality preserving projections for face recognition,” Neurocomputing 67, 443–449 (2005).

[CrossRef]

Y. D. Leeuw and D. Cohen, “Diffusion in sparse networks: linear to semilinear crossover,” Phys. Rev. E 86, 051120 (2012).

[CrossRef]

J. Ma and F.-X. L. Dimet, “Deblurring from highly incomplete measurements for remote sensing,” IEEE Trans. Geosci. Remote Sens. 47, 792–802 (2009).

[CrossRef]

A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 4–37 (2000).

[CrossRef]

R. P. W. Duin and E. Pekalska, “On refining dissimilarity matrices for an improved NN learning,” in Proceedings of 19th International Conf. on Pattern Recognition (IEEE, 2008), pp. 1–4.

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736–3745 (2006).

[CrossRef]

N. Gu, M. Fan, H. Qiao, and B. Zhang, “Discriminative sparsity preserving projections for semi-supervised dimensionality reduction,” IEEE Signal Process. Lett. 19, 391–394 (2012).

[CrossRef]

G. Feng, D. Hu, and Z. Zhou, “A direct locality preserving projections (DLPP) algorithm for image recognition,” Neural Process. Lett. 27, 247–255 (2008).

D. Hu, G. Feng, and Z. Zhou, “Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition,” Pattern Recogn. 40, 339–342 (2007).

[CrossRef]

L. Zhang, M. Yang, and X. Feng, “Sparse representation or collaborative representation: which helps face recognition?,” in Tenth IEEE International Conf. on Computer Vision (IEEE, 2011), pp. 1–8.

J. Yang, A. F. Frangi, J. Y. Yang, D. Zhang, and Z. Jin, “Kernel PCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and representation,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 230–244 (2005).

[CrossRef]

J. Wright, A. Yang, A. Ganesh, S. Shastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).

[CrossRef]

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in 2010 IEEE Conf. on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3360–3367.

N. Gu, M. Fan, H. Qiao, and B. Zhang, “Discriminative sparsity preserving projections for semi-supervised dimensionality reduction,” IEEE Signal Process. Lett. 19, 391–394 (2012).

[CrossRef]

D. Cai, X. He, and J. Han, “Semi-supervised discriminant analysis,” in Tenth IEEE International Conf. on Computer Vision (IEEE, 2007), pp. 1–7.

D. Cai, X. He, and L. Han, “Orthogonal Laplacianfaces for face recognition,” IEEE Trans. Image Process. 15, 3608–3614 (2006).

[CrossRef]

D. Cai, X. He, and L. Han, “Orthogonal Laplacianfaces for face recognition,” IEEE Trans. Image Process. 15, 3608–3614 (2006).

[CrossRef]

X. He and P. Niyogi, “Locality preserving projections,” Adv. Neural Inf. Process. Syst. 16, 1–8 (2003).

D. Cai, X. He, and J. Han, “Semi-supervised discriminant analysis,” in Tenth IEEE International Conf. on Computer Vision (IEEE, 2007), pp. 1–7.

X. He, D. Cai, S. Yan, and H.-J. Zhang, “Neighborhood preserving embedding,” in Tenth IEEE International Conf. on Computer Vision, Beijing, China (2005), pp. 1–8.

D. Cai, X. He, and K. Zhou, “Locality sensitive discriminant analysis,” in Proc. of International Joint Conf. on Artificial Intelligence, Hyderabad, India (2007), pp. 1–6.

P. Belhumeur, J. Hesanha, and D. Kreigman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997).

[CrossRef]

F. Yin, L. C. Jiao, F. Shang, S. Wang, and B. Hou, “Fast Fisher sparsity preserving projections,” Neural Comput. Appl.1–15 (2012).

[CrossRef]

G. Feng, D. Hu, and Z. Zhou, “A direct locality preserving projections (DLPP) algorithm for image recognition,” Neural Process. Lett. 27, 247–255 (2008).

D. Hu, G. Feng, and Z. Zhou, “Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition,” Pattern Recogn. 40, 339–342 (2007).

[CrossRef]

Y. Sun, J. Zhao, and Y. Hu, “Supervised sparsity preserving projections for face recognition,” Proc. SPIE 8009, 80092D (2011).

[CrossRef]

J. Wright, Y. Ma, J. Mairal, G. Spairo, T. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).

[CrossRef]

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in 2010 IEEE Conf. on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3360–3367.

H. Wang, C. Yuan, W. Hub, and C. Sun, “Supervised class-specific dictionary learning or sparse modeling in action recognition,” Pattern Recogn. 45, 3902–3911 (2012).

[CrossRef]

A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 4–37 (2000).

[CrossRef]

Z. L. Zhang, F. Yang, W. Tan, J. Jia, and J. Yang, “Gabor feature based face recognition using supervised locality preserving projection,” Signal Process. 87, 2473–2483 (2007).

[CrossRef]

F. Yin, L. C. Jiao, F. Shang, S. Wang, and B. Hou, “Fast Fisher sparsity preserving projections,” Neural Comput. Appl.1–15 (2012).

[CrossRef]

J. Yang, A. F. Frangi, J. Y. Yang, D. Zhang, and Z. Jin, “Kernel PCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and representation,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 230–244 (2005).

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P. Belhumeur, J. Hesanha, and D. Kreigman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997).

[CrossRef]

M. H. Yang, N. Ahuja, and D. Kriegman, “Face recognition using kernel eigenfaces,” in Proceedings of International Conf. on Image Processing, Vancouver, Canada (2000), pp. 1–4.

Y. D. Leeuw and D. Cohen, “Diffusion in sparse networks: linear to semilinear crossover,” Phys. Rev. E 86, 051120 (2012).

[CrossRef]

J. Cheng, Q. Liu, H. Lu, and Y.-W. Chen, “Supervised kernel locality preserving projections for face recognition,” Neurocomputing 67, 443–449 (2005).

[CrossRef]

Y. Lu, C. Lu, M. Qi, and S. Wang, “A supervised locality preserving projections based local matching algorithm for face recognition,” Lect. Notes Comput. Sci. 6059, 28–37 (2010).

[CrossRef]

J. Cheng, Q. Liu, H. Lu, and Y.-W. Chen, “Supervised kernel locality preserving projections for face recognition,” Neurocomputing 67, 443–449 (2005).

[CrossRef]

Y. Lu, C. Lu, M. Qi, and S. Wang, “A supervised locality preserving projections based local matching algorithm for face recognition,” Lect. Notes Comput. Sci. 6059, 28–37 (2010).

[CrossRef]

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in 2010 IEEE Conf. on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3360–3367.

J. Ma and F.-X. L. Dimet, “Deblurring from highly incomplete measurements for remote sensing,” IEEE Trans. Geosci. Remote Sens. 47, 792–802 (2009).

[CrossRef]

J. Wright, Y. Ma, J. Mairal, G. Spairo, T. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).

[CrossRef]

J. Wright, A. Yang, A. Ganesh, S. Shastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).

[CrossRef]

J. Wright, Y. Ma, J. Mairal, G. Spairo, T. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).

[CrossRef]

A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 4–37 (2000).

[CrossRef]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50, 072601 (2011).

[CrossRef]

B. Scholkopf, A. Smola, and K. R. Muller, “Nonlinear component analysis as a kernel eigenvalues problem,” Neural Comput. 10, 1299–1319 (1998).

[CrossRef]

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S. A. Nene, S. K. Nayar, and H. Murase, “Columbia Object Image Library (COIL100),” Department of Computer Science, Columbia University Tech. Rep. No. (1996).

S. A. Nene, S. K. Nayar, and H. Murase, “Columbia Object Image Library (COIL100),” Department of Computer Science, Columbia University Tech. Rep. No. (1996).

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50, 072601 (2011).

[CrossRef]

X. He and P. Niyogi, “Locality preserving projections,” Adv. Neural Inf. Process. Syst. 16, 1–8 (2003).

M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering,” in Advances in Neural Information Processing Systems 14, Vancouver, Canada (2001), pp. 585–591.

Y. Bengio, J. Palement, and P. Vincent, “Out-of-sample extensions for LLE, isomap, MOS, eigenmaps, and spectral clustering,” in Advances in Neural Information Processing Systems 6, Cambridge, MA (2003), p. 117.

M. Raazia, D. G. Paul, and N. W. Joseph, “A matching pursuit based similarity measure for fuzzy clustering and classification of signals,” in Proceedings of IEEE International Conference on Fuzzy Systems, Hong Kong, China (2008), pp. 1950–1955.

R. P. W. Duin and E. Pekalska, “On refining dissimilarity matrices for an improved NN learning,” in Proceedings of 19th International Conf. on Pattern Recognition (IEEE, 2008), pp. 1–4.

Y. Lu, C. Lu, M. Qi, and S. Wang, “A supervised locality preserving projections based local matching algorithm for face recognition,” Lect. Notes Comput. Sci. 6059, 28–37 (2010).

[CrossRef]

N. Gu, M. Fan, H. Qiao, and B. Zhang, “Discriminative sparsity preserving projections for semi-supervised dimensionality reduction,” IEEE Signal Process. Lett. 19, 391–394 (2012).

[CrossRef]

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving discriminant analysis for single training image face recognition,” Pattern Recogn. Lett. 31, 422–429 (2010).

[CrossRef]

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010).

[CrossRef]

M. Raazia, D. G. Paul, and N. W. Joseph, “A matching pursuit based similarity measure for fuzzy clustering and classification of signals,” in Proceedings of IEEE International Conference on Fuzzy Systems, Hong Kong, China (2008), pp. 1950–1955.

L. K. Saul and S. T. Roweis, “Think globally, fit locally: unsupervised learning of low dimensional manifolds,” J. Mach. Learn. Res. 4, 119–155 (2003).

[CrossRef]

S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290, 2323–2326 (2000).

[CrossRef]

L. K. Saul and S. T. Roweis, “Think globally, fit locally: unsupervised learning of low dimensional manifolds,” J. Mach. Learn. Res. 4, 119–155 (2003).

[CrossRef]

S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290, 2323–2326 (2000).

[CrossRef]

B. Scholkopf, A. Smola, and K. R. Muller, “Nonlinear component analysis as a kernel eigenvalues problem,” Neural Comput. 10, 1299–1319 (1998).

[CrossRef]

F. Yin, L. C. Jiao, F. Shang, S. Wang, and B. Hou, “Fast Fisher sparsity preserving projections,” Neural Comput. Appl.1–15 (2012).

[CrossRef]

J. Wright, A. Yang, A. Ganesh, S. Shastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).

[CrossRef]

B. Scholkopf, A. Smola, and K. R. Muller, “Nonlinear component analysis as a kernel eigenvalues problem,” Neural Comput. 10, 1299–1319 (1998).

[CrossRef]

J. Wright, Y. Ma, J. Mairal, G. Spairo, T. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).

[CrossRef]

M. Sugiyama, “Local Fisher discriminant analysis for supervised dimensionality reduction,” in Proc. of the 23th International Conf. on Machine Learning, Pittsburgh, USA (2006), pp. 1–8.

H. Wang, C. Yuan, W. Hub, and C. Sun, “Supervised class-specific dictionary learning or sparse modeling in action recognition,” Pattern Recogn. 45, 3902–3911 (2012).

[CrossRef]

Y. Sun, J. Zhao, and Y. Hu, “Supervised sparsity preserving projections for face recognition,” Proc. SPIE 8009, 80092D (2011).

[CrossRef]

Z. L. Zhang, F. Yang, W. Tan, J. Jia, and J. Yang, “Gabor feature based face recognition using supervised locality preserving projection,” Signal Process. 87, 2473–2483 (2007).

[CrossRef]

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recogn. 43, 331–341 (2010).

[CrossRef]

L. Qiao, S. Chen, and X. Tan, “Sparsity preserving discriminant analysis for single training image face recognition,” Pattern Recogn. Lett. 31, 422–429 (2010).

[CrossRef]

Y. Bengio, J. Palement, and P. Vincent, “Out-of-sample extensions for LLE, isomap, MOS, eigenmaps, and spectral clustering,” in Advances in Neural Information Processing Systems 6, Cambridge, MA (2003), p. 117.

E. J. Candès and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25 (2), 21–30 (2008).

[CrossRef]

H. Wang, C. Yuan, W. Hub, and C. Sun, “Supervised class-specific dictionary learning or sparse modeling in action recognition,” Pattern Recogn. 45, 3902–3911 (2012).

[CrossRef]

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in 2010 IEEE Conf. on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3360–3367.

Y. Lu, C. Lu, M. Qi, and S. Wang, “A supervised locality preserving projections based local matching algorithm for face recognition,” Lect. Notes Comput. Sci. 6059, 28–37 (2010).

[CrossRef]

F. Yin, L. C. Jiao, F. Shang, S. Wang, and B. Hou, “Fast Fisher sparsity preserving projections,” Neural Comput. Appl.1–15 (2012).

[CrossRef]

F. Xiang, Z. Wang, and X. Yuan, “Image reconstruction based on sparse and redundant representation model: local vs nonlocal,” Optik (2012).

[CrossRef]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50, 072601 (2011).

[CrossRef]

J. Wright, Y. Ma, J. Mairal, G. Spairo, T. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).

[CrossRef]

J. Wright, A. Yang, A. Ganesh, S. Shastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).

[CrossRef]

F. Xiang, Z. Wang, and X. Yuan, “Image reconstruction based on sparse and redundant representation model: local vs nonlocal,” Optik (2012).

[CrossRef]

J. Wright, Y. Ma, J. Mairal, G. Spairo, T. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).

[CrossRef]

X. He, D. Cai, S. Yan, and H.-J. Zhang, “Neighborhood preserving embedding,” in Tenth IEEE International Conf. on Computer Vision, Beijing, China (2005), pp. 1–8.

J. Wright, A. Yang, A. Ganesh, S. Shastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).

[CrossRef]

Z. L. Zhang, F. Yang, W. Tan, J. Jia, and J. Yang, “Gabor feature based face recognition using supervised locality preserving projection,” Signal Process. 87, 2473–2483 (2007).

[CrossRef]

Z. L. Zhang, F. Yang, W. Tan, J. Jia, and J. Yang, “Gabor feature based face recognition using supervised locality preserving projection,” Signal Process. 87, 2473–2483 (2007).

[CrossRef]

J. Yang, D. Zhang, and J.-Y. Yang, “Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 650–664 (2007).

[CrossRef]

J. Yang, A. F. Frangi, J. Y. Yang, D. Zhang, and Z. Jin, “Kernel PCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and representation,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 230–244 (2005).

[CrossRef]

H. Yu and J. Yang, “A direct LDA algorithm for high-dimensional data with application to face recognition,” Pattern Recogn. 34, 2067–2070 (2001).

[CrossRef]

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in 2010 IEEE Conf. on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3360–3367.

J. Yang, A. F. Frangi, J. Y. Yang, D. Zhang, and Z. Jin, “Kernel PCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and representation,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 230–244 (2005).

[CrossRef]

J. Yang, D. Zhang, and J.-Y. Yang, “Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 650–664 (2007).

[CrossRef]

L. Zhang, M. Yang, and X. Feng, “Sparse representation or collaborative representation: which helps face recognition?,” in Tenth IEEE International Conf. on Computer Vision (IEEE, 2011), pp. 1–8.

M. H. Yang, N. Ahuja, and D. Kriegman, “Face recognition using kernel eigenfaces,” in Proceedings of International Conf. on Image Processing, Vancouver, Canada (2000), pp. 1–4.

F. Yin, L. C. Jiao, F. Shang, S. Wang, and B. Hou, “Fast Fisher sparsity preserving projections,” Neural Comput. Appl.1–15 (2012).

[CrossRef]

H. Yu and J. Yang, “A direct LDA algorithm for high-dimensional data with application to face recognition,” Pattern Recogn. 34, 2067–2070 (2001).

[CrossRef]

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in 2010 IEEE Conf. on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3360–3367.

H. Wang, C. Yuan, W. Hub, and C. Sun, “Supervised class-specific dictionary learning or sparse modeling in action recognition,” Pattern Recogn. 45, 3902–3911 (2012).

[CrossRef]

F. Xiang, Z. Wang, and X. Yuan, “Image reconstruction based on sparse and redundant representation model: local vs nonlocal,” Optik (2012).

[CrossRef]

N. Gu, M. Fan, H. Qiao, and B. Zhang, “Discriminative sparsity preserving projections for semi-supervised dimensionality reduction,” IEEE Signal Process. Lett. 19, 391–394 (2012).

[CrossRef]

J. Yang, D. Zhang, and J.-Y. Yang, “Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 650–664 (2007).

[CrossRef]

J. Yang, A. F. Frangi, J. Y. Yang, D. Zhang, and Z. Jin, “Kernel PCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and representation,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 230–244 (2005).

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