A. Asuncion and D. Newman, UCI machine learning repository, 2007.

M. Balcan, A. Blum, P. P. Choi, J. Lafferty, B. Pantano et al., Person identification in webcam images: An, 2005.

M. Charikar, C. Chekuri, T. Feder, and R. Motwani, Incremental clustering and dynamic information retrieval, Proceedings of STOC, 1997.
DOI : 10.1137/s0097539702418498
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.3366

C. Cortes, M. Mohri, D. Pechyony, and A. Rastogi, Stability of transductive regression algorithms, Proceedings of the 25th international conference on Machine learning, ICML '08, 2008.
DOI : 10.1145/1390156.1390179

P. Drineas and M. W. Mahoney, On the Nyström method for approximating a Gram matrix for improved kernel-based learning, Proceedings of COLT, 2005.

S. Fine and K. Scheinberg, Efficient SVM training using low-rank kernel representations, Journal of Machine Learning Research, vol.2, pp.243-264, 2001.

C. Fowlkes, S. Belongie, F. Chung, and J. Malik, Spectral grouping using the Nyström method, IEEE Transactions on PAMI, vol.26, issue.2, 2004.

A. Goldberg, M. Li, and X. Zhu, Online Manifold Regularization: A New Learning Setting and Empirical Study, Proceeding of ECML, 2008.
DOI : 10.1007/978-3-540-87479-9_44

H. Grabner, C. Leistner, and H. Bischof, Semisupervised on-line boosting for robust tracking, Proceedings of ECCV, 2008.
DOI : 10.1007/978-3-540-88682-2_19
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.144.5747

R. M. Gray and D. L. Neuhoff, Quantization, IEEE Transactions on Information Theory, vol.44, issue.6, pp.2325-2383, 1998.
DOI : 10.1109/18.720541

B. Hendrickson and R. Leland, A multilevel algorithm for partitioning graphs, Proceedings of the 1995 ACM/IEEE conference on Supercomputing (CDROM) , Supercomputing '95, 1995.
DOI : 10.1145/224170.224228

G. Karypis and V. Kumar, A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.359-392, 1999.
DOI : 10.1137/S1064827595287997

B. Kveton, M. Valko, M. Phillipose, and L. Huang, Online semi-supervised perception: Real-time learning without explicit feedback, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Workshops, 2010.
DOI : 10.1109/CVPRW.2010.5543877
URL : https://hal.archives-ouvertes.fr/hal-00642999

B. Kveton, M. Valko, A. Rahimi, and L. Huang, Semi?supervised learning with maxmargin graph cuts, Proceedings of The Thirteenth International Conference on Artificial Intelligence and Statistics, pp.421-428, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00642891

D. Madigan, I. Raghavan, W. Dumouchel, M. Nason, C. Posse et al., Likelihood-based data squashing: a modeling approach to instance construction, Data Mining and Knowledge Discovery, vol.6, issue.2, pp.173-190, 2002.
DOI : 10.1023/A:1014095614948

P. Mitra, C. A. Murthy, and S. K. Pal, Densitybased multiscale data condensation, IEEE Transactions on PAMI, vol.24, issue.6, pp.1-14, 2002.
DOI : 10.1109/tpami.2002.1008381
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.6473

C. Williams and M. Seeger, Using the Nyström method to speed up kernel machines, Neural Information Processing Systems, 2001.

D. Yan, L. Huang, and M. I. Jordan, Fast approximate spectral clustering, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, 2009.
DOI : 10.1145/1557019.1557118
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.1997

X. Zhu, Semi-supervised learning literature survey, 2008.

X. Zhu, Z. Ghahramani, and J. Lafferty, Semisupervised learning using gaussian fields and harmonic functions, Proceedings of ICML, 2003.