A. Bellet, A. Habrard, and M. Sebban, A Survey on Metric Learning for Feature Vectors and Structured Data, 2013.

B. Kulis, Metric Learning: A Survey, Foundations and Trends?? in Machine Learning, vol.5, issue.4, pp.287-364, 2012.
DOI : 10.1561/2200000019

E. P. Xing, A. Y. Ng, M. I. Jordan, and S. J. Russell, Distance Metric Learning with Application to Clustering with Side-Information, Advances in Neural Information Processing Systems 15, pp.505-512, 2002.

M. Schultz and T. Joachims, Learning a Distance Metric from Relative Comparisons, Advances in Neural Information Processing Systems (NIPS) 16, 2003.

J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, Information-theoretic metric learning, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.209-216, 2007.
DOI : 10.1145/1273496.1273523

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.4476

P. Jain, B. Kulis, I. S. Dhillon, and K. Grauman, Online Metric Learning and Fast Similarity Search, Advances in Neural Information Processing Systems (NIPS) 21, pp.761-768, 2008.

K. Q. Weinberger and L. K. Saul, Distance Metric Learning for Large Margin Nearest Neighbor Classification, Journal of Machine Learning Research (JMLR), vol.10, pp.207-244, 2009.

Y. Ying, K. Huang, and C. Campbell, Sparse Metric Learning via Smooth Optimization, Advances in Neural Information Processing Systems (NIPS) 22, pp.2214-2222, 2009.

B. Mcfee and G. R. Lanckriet, Metric Learning to Rank, Proceedings of the 27th International Conference on Machine Learning, pp.775-782, 2010.

G. Chechik, U. Shalit, V. Sharma, and S. Bengio, An Online Algorithm for Large Scale Image Similarity Learning, Advances in Neural Information Processing Systems (NIPS) 22, pp.306-314, 2009.

A. M. Qamar, Generalized Cosine and Similarity Metrics: A supervised learning approach based on nearest-neighbors, 2010.
URL : https://hal.archives-ouvertes.fr/tel-00591988

A. Bellet, A. Habrard, and M. Sebban, Similarity Learning for Provably Accurate Sparse Linear Classification, Proceedings of the 29th International Conference on Machine Learning (ICML), pp.1871-1878, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00708401

Z. Guo and Y. Ying, Guaranteed Classification via Regularized Similarity Learning, Neural Computation, vol.22, issue.1, pp.497-522, 2014.
DOI : 10.1162/0899766053491896

URL : http://arxiv.org/abs/1306.3108

P. Kar and P. Jain, Similarity-based Learning via Data Driven Embeddings, Advances in Neural Information Processing Systems (NIPS), pp.1998-2006, 2011.

S. Shalev-shwartz, Y. Singer, and A. Y. Ng, Online and batch learning of pseudo-metrics, Twenty-first international conference on Machine learning , ICML '04, 2004.
DOI : 10.1145/1015330.1015376

Y. Wang, R. Khardon, D. Pechyony, and R. Jones, Generalization Bounds for Online Learning Algorithms with Pairwise Loss Functions, Proceedings of the 25th Annual Conference on Learning Theory (COLT), 2012, pp.13-14

W. Bian and D. Tao, Learning a Distance Metric by Empirical Loss Minimization, Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), pp.1186-1191, 2011.

W. Bian and D. Tao, Constrained Empirical Risk Minimization Framework for Distance Metric Learning, IEEE Transactions on Neural Networks and Learning Systems, vol.23, issue.8, pp.1194-1205, 2012.
DOI : 10.1109/TNNLS.2012.2198075

R. Jin, S. Wang, and Y. Zhou, Regularized Distance Metric Learning: Theory and Algorithm, Advances in Neural Information Processing Systems (NIPS) 22, pp.862-870, 2009.

O. Bousquet and A. Elisseeff, Stability and Generalization, Journal of Machine Learning Research (JMLR), vol.2, pp.499-526, 2002.

H. Xu, C. Caramanis, and S. Mannor, Sparse Algorithms Are Not Stable: A No-Free-Lunch Theorem, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.34, pp.187-193, 2012.

G. Qi, J. Tang, Z. Zha, T. Chua, and H. Zhang, An Efficient Sparse Metric Learning in High-Dimensional Space via l1-Penalized Log- Determinant Regularization, Proceedings of the 26th International Conference on Machine Learning (ICML), 2009.

A. Bellet and A. Habrard, Robustness and generalization for metric learning, Neurocomputing, vol.151, 2012.
DOI : 10.1016/j.neucom.2014.09.044

URL : https://hal.archives-ouvertes.fr/hal-01075370

Q. Cao, Z. Guo, and Y. Ying, Generalization bounds for metric and similarity learning, Machine Learning, vol.13, issue.1, 2012.
DOI : 10.1007/s10994-015-5499-7

H. Xu, S. Mannor, and G. Robustness, Robustness and generalization, Machine Learning, vol.75, issue.7, pp.391-423, 2012.
DOI : 10.1007/s10994-011-5268-1

A. N. Kolmogorov and V. M. , Tikhomirov, ?-entropy and ?-capacity of sets in functional spaces, pp.277-364, 1961.

A. W. Van-der-vaart and J. A. Wellner, Weak convergence and empirical processes, 2000.
DOI : 10.1007/978-1-4757-2545-2

G. Kunapuli and J. Shavlik, Mirror Descent for Metric Learning: A Unified Approach, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD), pp.859-874, 2012.
DOI : 10.1007/978-3-642-33460-3_60

J. Wang, A. Woznica, and A. Kalousis, Parametric Local Metric Learning for Nearest Neighbor Classification, Advances in Neural Information Processing Systems (NIPS) 25, pp.1610-1618, 2012.

B. Kulis, K. Saenko, and T. , What you saw is not what you get: Domain adaptation using asymmetric kernel transforms, CVPR 2011, pp.1785-1792, 2011.
DOI : 10.1109/CVPR.2011.5995702

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.2663

B. Geng, D. Tao, and C. Xu, DAML: Domain Adaptation Metric Learning, IEEE Transactions on Image Processing, vol.20, issue.10, pp.2980-2989, 2011.
DOI : 10.1109/TIP.2011.2134107

B. Q. Feng, Equivalence constants for certain matrix norms, Linear Algebra and its Applications, vol.374, pp.247-253, 2003.
DOI : 10.1016/S0024-3795(03)00616-5

A. Klaus and C. Li, ) norm, Linear and Multilinear Algebra, vol.33, issue.4, pp.315-332, 1995.
DOI : 10.1080/03081087308817003