J. B. Tenenbaum, V. De-silva, and J. C. Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2319-2323, 2000.
DOI : 10.1126/science.290.5500.2319

S. T. Roweis and L. K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol.290, issue.5500, pp.2323-2326, 2000.
DOI : 10.1126/science.290.5500.2323

M. Belkin and P. Niyogi, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, Neural Computation, vol.15, issue.6, pp.1373-1396, 2003.
DOI : 10.1126/science.290.5500.2319

S. Yan, D. Xu, B. Zhang, H. J. Zhang, Q. Yang et al., Graph Embedding and Extensions: A General Framework for Dimensionality Reduction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.1, pp.40-51, 2007.
DOI : 10.1109/TPAMI.2007.250598

Q. Hua, L. Bai, X. Z. Wang, and Y. Liu, Local similarity and diversity preserving discriminant projection for face and handwriting digits recognition, Neurocomputing, vol.86, pp.150-157, 2012.
DOI : 10.1016/j.neucom.2012.01.031

W. Yang, C. Sun, and L. Zhang, A multi-manifold discriminant analysis method for image feature extraction, Pattern Recognition, vol.44, issue.8, pp.1649-1657, 2011.
DOI : 10.1016/j.patcog.2011.01.019

Z. Zhang, M. Zhao, and T. Chow, Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition, Neural Networks, vol.36, pp.97-111, 2012.
DOI : 10.1016/j.neunet.2012.09.010

Q. Gao, J. Ma, H. Zhang, X. Gao, and Y. Liu, Stable orthogonal local discriminant embedding for linear dimensionality reduction, IEEE Transactions on Image Processing, vol.22, issue.7, pp.2521-2531, 2013.

B. Raducanu and F. Dornaika, A supervised non-linear dimensionality reduction approach for manifold learning, Pattern Recognition, vol.45, issue.6, pp.2432-2444, 2012.
DOI : 10.1016/j.patcog.2011.12.006

Y. Bengio, J. F. Paiement, P. Vincent, O. Delalleau, N. L. Roux et al., Out-of-sample extensions for LLE, ISOMAP, MDS, Eigenmaps, and Spectral Clustering, Adv. Neural Inf. Process. Syst, pp.177-184, 2004.

G. H. Chen, C. Wachinger, and P. Golland, Sparse Projections of Medical Images onto Manifolds, Information Processing in Medical Imaging -23rd International Conference, IPMI 2013, pp.292-303, 2013.
DOI : 10.1007/978-3-642-38868-2_25

H. Qiao, P. Zhang, D. Wang, and B. Zhang, An explicit nonlinear mapping for manifold learning, pp.51-63, 2013.

B. Peherstorfer, D. Pflüger, and H. J. Bungartz, A sparse-grid-based outof-sample extension for dimensionality reduction and clustering with laplacian eigenmaps, AI 2011: Advances in Artificial Intelligence -24th Australasian Joint Conference, pp.112-121, 2011.

H. Strange and R. Zwiggelaar, A generalised solution to the out-of-sample extension problem in manifold learning, Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, p.3603, 2011.

M. W. Trosset and C. E. Priebe, The out-of-sample problem for classical multidimensional scaling, Computational Statistics & Data Analysis, vol.52, issue.10, pp.4635-4642, 2008.
DOI : 10.1016/j.csda.2008.02.031

T. J. Chin and D. Suter, Out-of-Sample Extrapolation of Learned Manifolds, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.9, pp.1547-1556, 2008.
DOI : 10.1109/TPAMI.2007.70813

K. Q. Weinberger and L. K. Saul, Unsupervised Learning of Image Manifolds by Semidefinite Programming, International Journal of Computer Vision, vol.26, issue.1, pp.77-90, 2006.
DOI : 10.1007/s11263-005-4939-z

X. He and P. Niyogi, Locality Preserving Projections, Advances in Neural Information Processing Systems 16, 2004.

X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, Face recognition using Laplacianfaces, IEEE Trans. Pattern Anal. Mach. Intell, vol.27, issue.3, pp.328-340, 2005.

D. Cai, X. He, J. Han, and H. Zhang, Orthogonal Laplacianfaces for Face Recognition, IEEE Transactions on Image Processing, vol.15, issue.11, pp.3608-3614, 2006.
DOI : 10.1109/TIP.2006.881945

R. Wang and X. Chen, Manifold discriminant analysis, CVPR, pp.429-436, 2009.

D. Xu, S. Yan, D. Tao, S. Lin, and H. Zhang, Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content- Based Image Retrieval, IEEE Transactions on Image Processing, vol.16, issue.11, pp.2811-2821, 2007.
DOI : 10.1109/TIP.2007.906769

M. D. Buhmann, Radial Basis Functions, 2003.
DOI : 10.1017/cbo9780511543241

M. K. Kozlov, S. P. Tarasov, and L. Khachiyan, The polynomial solvability of convex quadratic programming, USSR Computational Mathematics and Mathematical Physics, vol.20, issue.5, pp.223-228, 1980.
DOI : 10.1016/0041-5553(80)90098-1

C. Saunders, A. Gammerman, and V. Vovk, Ridge regression learning algorithm in dual variables, Proceedings of the Fifteenth International Conference on Machine Learning, pp.515-521, 1998.

X. Zhu, Z. Ghahramani, J. D. Lafferty, P. N. Belhumeur, and D. J. Kriegman, Semi-supervised learning using gaussian fields and harmonic functions From few to many: Illumination cone models for face recognition under variable lighting and pose, Machine Learning, Proceedings of the Twentieth International Conference, pp.912-919, 2001.

B. Leibe and B. Schiele, Analyzing appearance and contour based methods for object categorization, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., pp.16-22, 2003.
DOI : 10.1109/CVPR.2003.1211497

S. A. Nene, S. K. Nayar, and H. Murase, Columbia Object Image Library (COIL-20), Tech. Rep, 1996.