.. .. Analyse-théorique,

. .. Expériences,

. .. Conclusion,

, Apprentissage de Métriques par Transfert d'Hypothèses 85

. , Apprentissage de Métriques par Transfert d'Hypothèses avec, vol.87

.. .. Analyse-par-stabilité-uniforme,

. .. Exemples,

. .. Expériences,

. .. Conclusion,

, III Apprentissage de MétriquesMétriquesà Comportement Contôlé, vol.114

, Apprentissage de Métriques par Régression 117

, Apprendre une Métrique en Utilisant des Points Virtuels, p.118

.. .. Analyse-théorique,

. .. Expériences,

. .. Conclusion,

. .. Cadre-de-travail,

. , Discussion sur les Aspects Théoriques

. .. Expériences,

R. Achanta and S. Süsstrunk, Saliency detection using maximum symmetric surround, 2010 IEEE International Conference on Image Processing, vol.64, p.73, 2010.

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, Contour detection and hierarchical image segmentation, IEEE transactions on pattern analysis and machine intelligence, vol.33, p.73, 2011.

J. Audiffren and H. Kadri, Stability of multi-task kernel regression algorithms, Proc. of ACML, p.125, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00834994

M. Balcan, A. Blum, and N. Srebro, Improved guarantees for learning via similarity functions, Computer Science Department, vol.56, p.206, 2008.

L. Peter, S. Bartlett, and . Mendelson, Rademacher and gaussian complexities: Risk bounds and structural results, Journal of Machine Learning Research, vol.3, p.179, 2002.

A. Bellet and A. Habrard, Robustness and generalization for metric learning, Neurocomputing, vol.151, p.54, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01075370

A. Bellet, A. Habrard, and M. Sebban, Learning good edit similarities with generalization guarantees, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, p.56, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00608631

A. Bellet, A. Habrard, and M. Sebban, Similarity learning for provably accurate sparse linear classification, ICML 2012, vol.44, p.136, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00708401

A. Bellet, A. Habrard, and M. Sebban, Metric Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, p.35, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01121733

S. Ben, -. , and R. Urner, Domain adaptation as learning with auxiliary information, New Directions in Transfer and Multi-Task-Workshop@ NIPS, 2013.

S. Ben-david, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira et al., A theory of learning from different domains, Machine Learning, vol.79, p.94, 2010.

J. Benamou, G. Carlier, M. Cuturi, L. Nenna, and G. Peyré, Iterative bregman projections for regularized transportation problems, SISC, p.140, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01096124

G. Bennett, Probability inequalities for the sum of independent random variables, Journal of the American Statistical Association, vol.57, issue.297, p.26, 1962.

C. Berlind and R. Urner, Active nearest neighbors in changing environments, Proceedings of the 32nd International Conference on Machine Learning, vol.159, p.206, 2015.

J. Bi, D. Wu, L. Lu, M. Liu, Y. Tao et al., Adaboost on low-rank psd matrices for metric learning, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, p.53, 2011.

K. Bitsakos, C. Fermüller, and Y. Aloimonos, An experimental study of color-based segmentation algorithms based on the mean-shift concept, European conference on Computer vision, vol.64, p.80, 2010.

J. Bohné, Y. Ying, S. Gentric, and M. Pontil, Large margin local metric learning, European Conference on Computer Vision, vol.45, p.107, 2014.

M. Karsten, A. Borgwardt, . Gretton, J. Malte, H. Rasch et al., Integrating structured biological data by kernel maximum mean discrepancy, Bioinformatics, vol.22, issue.14, p.58, 2006.

S. Boucheron, G. Lugosi, and O. Bousquet, Concentration inequalities, Advanced Lectures on Machine Learning, p.26, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00777381

O. Bousquet, Concentration inequalities and empirical processes theory applied to the analysis of learning algorithms, p.102, 2002.

O. Bousquet and A. Elisseeff, Stability and generalization, Journal of Machine Learning Research, vol.2, p.191, 2002.

O. Bousquet and A. Elisseeff, Stability and generalization, Journal of Machine Learning Research, vol.2, p.125, 2002.

L. Bruzzone and M. Marconcini, Domain adaptation problems: A DASVM classification technique and a circular validation strategy, IEEE PAMI, vol.32, issue.5, p.151

J. Gertjan, J. Burghouts, and . Geusebroek, Performance evaluation of local colour invariants, Computer Vision and Image Understanding, vol.113, issue.1, p.72, 2009.

G. Canas and L. Rosasco, Learning probability measures with respect to optimal transport metrics, NIPS, vol.139, p.140, 2012.

Q. Cao, Y. Ying, and P. Li, Similarity metric learning for face recognition, Proc. of the IEEE International Conference on Computer Vision (ICCV), vol.86, p.107, 2013.

Q. Cao, Y. Ying, and P. Li, Similarity metric learning for face recognition, Proceedings of the IEEE International Conference on Computer Vision, p.57, 2013.

Q. Cao, Z. Guo, and Y. Ying, Generalization bounds for metric and similarity learning, Machine Learning, vol.102, p.187, 2016.

H. Chang and D. Yeung, Locally linear metric adaptation for semi-supervised clustering, Proceedings of the twenty-first international conference on Machine learning, vol.20, p.56, 2004.

H. Chang and D. Yeung, Locally smooth metric learning with application to image retrieval, IEEE 11th International Conference on Computer Vision, vol.45, p.57, 2007.

G. Chechik, V. Sharma, U. Shalit, and S. Bengio, Large scale online learning of image similarity through ranking, Iberian Conference on Pattern Recognition and Image Analysis, vol.44, p.53, 2009.

G. Chechik, V. Sharma, U. Shalit, and S. Bengio, Large scale online learning of image similarity through ranking, Journal of Machine Learning Research, vol.11, p.57, 2010.

G. Stéphan-clémençon, N. Lugosi, and . Vayatis, Ranking and empirical minimization of u-statistics. The Annals of Statistics, p.55, 2008.

C. Cortes and V. Vapnik, Support-vector networks, Machine learning, vol.20, issue.3, p.36, 1995.

C. Cortes, M. Mohri, and J. Weston, A general regression technique for learning transductions, Proc. of ICML, p.120, 2005.
DOI : 10.1145/1102351.1102371

C. Cortes, M. Mohri, and J. Weston, A general regression framework for learning string-to-string mappings, Predicting Structured Data, p.189, 2007.

N. Courty, R. Flamary, and D. Tuia, Domain adaptation with regularized optimal transport, ECML PKDD, vol.140, p.150, 2014.
DOI : 10.1007/978-3-662-44848-9_18
URL : https://hal.archives-ouvertes.fr/hal-01018698

N. Bibliography, R. Courty, D. Flamary, and . Tuia, Domain adaptation with regularized optimal transport, Proc. of ECML/PKDD, vol.121, p.138, 2014.

M. Thomas, P. E. Cover, and . Hart, Nearest neighbor pattern classification, IEEE Trans. Information Theory, vol.13, issue.1, p.38, 1967.

M. Cuturi, Sinkhorn distances: Lightspeed computation of optimal transport, NIPS, 2013a. 139, vol.140, p.150

M. Cuturi and A. Doucet, Fast computation of wasserstein barycenters, ICML, p.140, 2014.

M. Cuturi and G. Peyré, A smoothed dual approach for variational wasserstein problems, SIIMS, p.140, 2016.
DOI : 10.1137/15m1032600
URL : https://hal.archives-ouvertes.fr/hal-01188954

M. Cuturi, Sinkhorn distances: Lightspeed computation of optimal transport, Proc. of NIPS, pp.2292-2300, 2013.

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol.1, p.72, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548512

J. Davis, B. Kulis, P. Jain, S. Sra, and . Dhillon, Informationtheoretic metric learning, Proceedings of the 24th international conference on Machine learning, vol.43, p.131, 2007.

F. Deng, S. J. Kim, Y. Tai, and M. Brown, ACCV, chapter Color-Aware Regularization for Gradient Domain Image Manipulation, p.153, 2012.
DOI : 10.1007/978-3-642-37447-0_30

. Terry-e-dielman, Least absolute value regression: recent contributions, Journal of Statistical Computation and Simulation, vol.75, issue.4, p.32, 2005.

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang et al., Decaf: A deep convolutional activation feature for generic visual recognition, ICML, p.150, 2014.

B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, Unsupervised visual domain adaptation using subspace alignment, ICCV, p.150, 2013.
DOI : 10.1109/iccv.2013.368
URL : https://hal.archives-ouvertes.fr/hal-00869417

S. Ferradans, N. Papadakis, G. Peyré, and J. Aujol, , vol.144, p.152

M. Frank and P. Wolfe, An algorithm for quadratic programming, NRL, vol.3, issue.1-2
DOI : 10.1002/nav.3800030109

Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci, vol.55, issue.1, p.31, 1997.

Y. Freund and R. E. Schapire, Large margin classification using the perceptron algorithm, Machine learning, vol.37, issue.3, p.53, 1999.

J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, vol.28, p.31, 2000.

C. Frogner, C. Zhang, H. Mobahi, M. Araya, and T. Poggio, Learning with a wasserstein loss

K. Fukunaga and L. Hostetler, The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Transactions on information theory, vol.21, issue.1, p.79, 1975.

B. Geng, D. Tao, and C. Xu, DAML: domain adaptation metric learning, IEEE Transactions on Image Processing, vol.20, issue.10, p.57, 2011.

A. Globerson and . Sam-t-roweis, Metric learning by collapsing classes, Advances in neural information processing systems, vol.48, p.131, 2005.

J. Goldberger, G. E. Hinton, R. Sam-t-roweis, and . Salakhutdinov, Neighbourhood components analysis, Advances in neural information processing systems, vol.43, p.56, 2004.

B. Gong, Y. Shi, F. Sha, and K. Grauman, Geodesic flow kernel for unsupervised domain adaptation, 2012 IEEE Conference on Computer Vision and Pattern Recognition, vol.110, p.150, 2012.

G. Griffin, A. Holub, and P. Perona, Caltech-256 object category dataset, p.39, 2007.

C. Zheng-, Y. Guo, and . Ying, Guaranteed classification via regularized similarity learning, Neural computation, vol.26, issue.3, p.56, 2014.

J. Hoffman, E. Rodner, J. Donahue, K. Saenko, and T. Darrell, Efficient learning of domain-invariant image representations. CoRR, abs/1301, vol.3224, p.110, 2013.

M. Huang, H. Liu, G. Cui, M. Ronnier-luo, and . Melgosa, Evaluation of threshold color differences using printed samples, JOSA A, vol.29, issue.6, p.72, 2012.

J. Peter and . Huber, Robust estimation of a location parameter, Annals of Mathematical Statistics, vol.35, issue.1, pp.73-101, 1932.

Z. Huo, F. Nie, and H. Huang, Robust and effective metric learning using capped trace norm: Metric learning via capped trace norm, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol.44, p.57, 2016.

A. Ilie and G. Welch, Ensuring color consistency across multiple cameras, Tenth IEEE International Conference on Computer Vision (ICCV'05, vol.1, p.78, 2005.

M. Jaggi, Revisiting frank-wolfe: Projection-free sparse convex optimization, ICML, 2013.

P. Jain, B. Kulis, K. Inderjit-s-dhillon, and . Grauman, Online metric learning and fast similarity search, Advances in neural information processing systems, p.53, 2009.

R. Jin, S. Wang, and Y. Zhou, Regularized distance metric learning: Theory and algorithm, Advances in neural information processing systems, vol.107, p.126, 2009.

L. Kantorovich, On the translocation of masses, vol.37, p.141, 1942.

P. Kar and P. Jain, Similarity-based learning via data driven embeddings, Advances in neural information processing systems, vol.121, p.129, 1938.

D. Kedem, S. Tyree, F. Sha, G. R. Lanckriet, and . Weinberger, Non-linear metric learning, Advances in Neural Information Processing Systems, vol.45, p.129, 2012.

R. Khan, J. Van-de-weijer, F. Shahbaz-khan, D. Muselet, C. Ducottet et al., Discriminative color descriptors, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.73, 2013.
URL : https://hal.archives-ouvertes.fr/ujm-00854763

S. Kim, H. T. Lin, Z. Lu, S. Süsstrunk, S. Lin et al., A new in-camera imaging model for color computer vision and its application, IEEE Trans. Pattern Anal. Mach. Intell, vol.34, issue.12, p.73, 2012.

S. Kim, H. T. Lin, Z. Lu, S. Süsstrunk, S. Lin et al., A new in-camera imaging model for color computer vision and its application, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.12, p.73, 2012.

A. Philip and . Knight, The sinkhorn-knopp algorithm: convergence and applications, SIAM Journal on Matrix Analysis and Applications, vol.30, issue.1, pp.261-275, 2008.

B. Kulis, K. Saenko, and T. Darrell, What you saw is not what you get: Domain adaptation using asymmetric kernel transforms, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, vol.44, p.138, 2011.

M. Kusner, S. Tyree, Q. Kilian, and . Weinberger, Stochastic neighbor compression, Proceedings of the 31st international conference on machine learning (ICML-14), p.124, 2014.

I. Kuzborskij and F. Orabona, Stability and hypothesis transfer learning, Proc. of the 30th International Conference on Machine Learning, ICML 2013, vol.86, p.94, 2013.

I. Kuzborskij and F. Orabona, Learning by transferring from auxiliary hypotheses. CoRR, abs/1412.1619, vol.101, p.112, 2014.

J. Langford, Tutorial on practical prediction theory for classification, Journal of machine learning research, vol.6, p.26, 2005.

. Re-larraín, J. D. Schaefer, and . Reed, Use of digital images to estimate cie color coordinates of beef, Food Research International, vol.41, issue.4, p.74, 2008.

N. Marc-t-law, M. Thome, and . Cord, Quadruplet-wise image similarity learning, Proceedings of the IEEE International Conference on Computer Vision, p.51, 2013.

N. Marc-t-law, M. Thome, and . Cord, Fantope regularization in metric learning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol.44, p.57, 2014.

Y. Lei and Y. Ying, Generalization analysis of multi-modal metric learning, Analysis and Applications, p.96, 2015.

K. Leon, D. Mery, F. Pedreschi, and J. Leon, Color measurement in l? a? b? units from rgb digital images, Food research international, vol.39, issue.10, p.74, 2006.

M. Lichman, UCI machine learning repository, vol.109, p.128, 2013.

P. Stuart and . Lloyd, Least squares quantization in PCM, IEEE Trans. Information Theory, vol.28, issue.2, p.34, 1982.

. David-g-lowe, Distinctive image features from scale-invariant keypoints. International journal of computer vision, vol.60, p.72, 2004.

C. Prasanta and . Mahalanobis, On the generalized distance in statistics, Proceedings of the National Institute of Sciences (Calcutta), vol.2, p.109, 1936.

Y. Mansour, M. Mohri, and A. Rostamizadeh, Domain adaptation: Learning bounds and algorithms, COLT 2009-The 22nd Conference on Learning Theory, p.40, 2009.

R. Mccann, A convexity principle for interacting gases, Advances in Mathematics, vol.128, issue.1

C. Mcdiarmid, Surveys in Combinatorics, chapter On the method of bounded differences, vol.26, p.164, 1989.

M. Melgosa, R. Huertas, and R. Berns, Performance of recent advanced colordifference formulas using the standardized residual sum of squares index, JOSA A, vol.25, issue.7, p.77, 2008.

A. Mojsilovic, A computational model for color naming and describing color composition of images, IEEE Transactions on Image processing, vol.14, issue.5, p.73, 2005.

J. Mueller and T. Jaakkola, Principal differences analysis: Interpretable characterization of differences between distributions

H. Albert and . Munsell, A pigment color system and notation, The American Journal of Psychology, vol.23, issue.2, pp.236-244

M. Nicolae, E. Gaussier, A. Habrard, and M. Sebban, Joint semisupervised similarity learning for linear classification, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, vol.105, p.107, 2015.

S. J. Pan and Q. Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Eng, vol.22, issue.10, p.39, 2010.

S. Parameswaran, . Kilian, and . Weinberger, Large margin multi-task metric learning, Advances in neural information processing systems, vol.58, p.107, 2010.

P. Pérez, M. Gangnet, and A. Blake, Poisson image editing, ACM Trans. on Graphics, vol.22, issue.3, p.155, 2003.

M. Perrot and A. Habrard, Apprentissage de mtriques par rgression, Conférence francophone sur l'Apprentissage Automatique (CAp-15), 2015.

M. Perrot and A. Habrard, Transfert d'informations en apprentissage de mtriques : une analyse thorique, Conférence francophone sur l'Apprentissage Automatique, 2015.

M. Perrot and A. Habrard, Regressive virtual metric learning, Advances in Neural Information Processing Systems (NIPS-15), pp.1810-1818, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01220665

M. Perrot and A. Habrard, A theoretical analysis of metric hypothesis transfer learning, Proceedings of the 32nd International Conference on Machine Learning (ICML15), pp.1708-1717, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01175610

M. Perrot and A. Habrard, Bornes en généralisationgénéralisationà convergence rapide pour le transfert d'hypothèses en apprentissage de métriques, Conférence francophone sur l'Apprentissage Automatique (CAp-16), 2016.

M. Perrot, A. Habrard, D. Muselet, and M. Sebban, Freely avaible on the authors' personal web pages, vol.157, p.204, 2014.

M. Perrot, A. Habrard, D. Muselet, and M. Sebban, Modeling perceptual color differences by local metric learning, European Conference on Computer Vision (ECCV-15), pp.96-111, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01009610

M. Perrot, A. Habrard, D. Muselet, and M. Sebban, Modélisation de distances couleur uniformes par apprentissage de métriques locales, Conférence francophone sur l'Apprentissage Automatique (CAp-14), 2014.

M. Perrot, N. Courty, R. Flamary, and A. Habrard, Mapping estimation of discrete optimal transport, Advances in Neural Information Processing Systems (NIPS16), 2016.

A. Mustafa-qamar and E. Gaussier, Online and batch learning of generalized cosine similarities, Ninth IEEE International Conference on Data Mining, p.44, 2009.

A. Mustafa-qamar, E. Gaussier, J. Chevallet, and J. Lim, Similarity learning for nearest neighbor classification, Eighth IEEE International Conference on Data Mining, vol.44, p.56, 2008.

S. Reich, A nonparametric ensemble transform method for bayesian inference. SISC, vol.140, p.142, 2013.

K. Saenko, B. Kulis, M. Fritz, and T. Darrell, Adapting visual category models to new domains, European conference on computer vision, vol.57, p.138, 2010.

B. Schölkopf, A. Smola, and K. Müller, Kernel principal component analysis, International Conference on Artificial Neural Networks, p.58, 1997.

B. Schölkopf, A. J. Smola, and K. Müller, Kernel principal component analysis, Artificial Neural Networks-ICANN '97, 7th International Conference, p.36, 1997.

V. Seguy and M. Cuturi, Principal geodesic analysis for probability measures under the optimal transport metric

M. Semerci and E. Alpayd?n, Mixtures of large margin nearest neighbor classifiers, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, vol.45, p.82, 2013.

S. Shalev, -. Shwartz, and S. Ben-david, Understanding Machine Learning-From Theory to Algorithms, chapter Regularization and Stability, vol.88, p.179, 2014.

S. Shalev, -. Shwartz, and S. Ben-david, Understanding machine learning: From theory to algorithms, p.28, 2014.

S. Shalev-shwartz, Y. Singer, and A. Ng, Online and batch learning of pseudometrics, Proceedings of the twenty-first international conference on Machine learning, p.52, 2004.

G. Sharma and W. Wu, The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations, Color Research & Application, vol.30, p.78, 2005.

C. Shen, J. Kim, L. Wang, and A. Hengel, Positive semidefinite metric learning with boosting, Advances in neural information processing systems, vol.43, p.53, 2009.

C. Shen, J. Kim, L. Wang, and A. Van-den-hengel, Positive semidefinite metric learning using boosting-like algorithms, Journal of Machine Learning Research, vol.13, p.107, 2012.

Y. Shi, A. Bellet, and F. Sha, Sparse compositional metric learning, vol.43, p.129, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01430847

J. Solomon, R. Rustamov, G. Leonidas, and A. Butscher, Wasserstein propagation for semisupervised learning, ICML, vol.139, p.140, 2014.

J. Solomon, F. De-goes, G. Peyré, M. Cuturi, A. Butscher et al., Convolutional wasserstein distances. ACM Trans. on Graphics, vol.34, issue.4

N. Srebro, K. Sridharan, and A. Tewari, Smoothness, low noise and fast rates, Proc. of NIPS, vol.96, p.106, 2010.

N. Srebro, K. Sridharan, and A. Tewari, Optimistic rates for learning with a smooth loss. CoRR, abs/1009, vol.3896, p.163, 2010.

N. Srebro, K. Sridharan, and A. Tewari, Smoothness, low noise and fast rates, Advances in neural information processing systems, vol.159, p.206, 2010.

M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, A standard default color space for the internet: sRGB, p.73, 1996.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), vol.31, p.33, 1996.

P. Tseng, Convergence of a block coordinate descent method for nondifferentiable minimization, Journal of optimization theory and applications, vol.109, issue.3, p.143, 2001.

. Int, Telecommunications Union. Parameter values for the hdtv standards for production and international programme exchange, itu-r recommendation bt.709-4, p.73, 2000.

G. Leslie and . Valiant, A theory of the learnable, Communications of the ACM, vol.27, issue.11, p.25, 1984.

K. Van-de-sande, T. Gevers, and C. Snoek, Evaluating color descriptors for object and scene recognition, IEEE transactions on pattern analysis and machine intelligence, vol.32, p.72, 2010.

J. Van-de-weijer, T. Gevers, and J. Geusebroek, Edge and corner detection by photometric quasi-invariants, IEEE transactions on pattern analysis and machine intelligence, vol.27, p.72, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548526

J. Van-de-weijer, T. Gevers, and A. Bagdanov, Boosting color saliency in image feature detection, IEEE transactions on pattern analysis and machine intelligence, vol.28, p.72, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00548615

. Aad-w-van-der, J. A. Vaart, and . Wellner, Weak convergence, Weak Convergence and Empirical Processes, vol.70, p.164, 1996.

V. Vapnik, Estimation of Dependences Based on Empirical Data, p.26, 1982.

V. N. Vapnik, A. Ya, and . Chervonenkis, On the uniform convergence of relative frequencies of events to their probabilities, Theory of Probability and its Applications, vol.16, p.26, 1971.

V. N. Vapnik, Statistical Learning Theory, p.56, 1998.

C. Villani, Optimal transport: old and new. Grund. der mathematischen Wissenschaften, vol.140, p.141, 2009.

C. Villani, Optimal transport: old and new, vol.338, p.122, 2008.

J. Wang, A. Kalousis, and A. Woznica, Parametric local metric learning for nearest neighbor classification, Advances in Neural Information Processing Systems, vol.45, p.56, 2012.

Q. Wang, F. Chen, Q. Gao, X. Gao, and F. Nie, On the schatten norm for matrix based subspace learning and classification, Neurocomputing, p.34, 2016.

Q. Bibliography-kilian, L. Weinberger, and . Saul, Fast solvers and efficient implementations for distance metric learning, Proceedings of the 25th international conference on Machine learning, vol.45, p.64, 2008.

Q. Kilian, L. Weinberger, and . Saul, Distance metric learning for large margin nearest neighbor classification, Journal of Machine Learning Research, vol.10, p.50, 2009.

Q. Kilian, G. Weinberger, and . Tesauro, Metric learning for kernel regression, AISTATS, p.58, 2007.

Q. Kilian, J. Weinberger, L. Blitzer, and . Saul, Distance metric learning for large margin nearest neighbor classification, Advances in neural information processing systems, vol.43, p.129, 2005.

G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas, p.72, 2000.

P. Eric, . Xing, Y. Andrew, . Ng, S. Michael-i-jordan et al., Distance metric learning with application to clustering with side-information, Advances in neural information processing systems, vol.15, p.56, 2002.

C. Xiong, D. Johnson, R. Xu, and J. J. Corso, Random forests for metric learning with implicit pairwise position dependence, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, p.45, 2012.

Y. Xiong, K. Saenko, T. Darrell, and T. Zickler, From pixels to physics: Probabilistic color de-rendering, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, p.73, 2012.

H. Xu and S. Mannor, Robustness and generalization, COLT 2010-The 23rd Conference on Learning Theory, p.26, 2010.

H. Xu and S. Mannor, Robustness and generalization, Machine learning, vol.86, issue.3, p.70, 2012.

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, vol.34, p.27, 2012.

W. Xue, X. Mou, L. Zhang, and X. Feng, Perceptual fidelity aware mean squared error, Proceedings of the IEEE International Conference on Computer Vision, pp.705-712, 2013.

Y. Ying, K. Huang, and C. Campbell, Sparse metric learning via smooth optimization, Advances in neural information processing systems, vol.44, p.107, 2009.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.68, issue.1, p.34, 2006.

V. Zantedeschi, R. Emonet, and M. Sebban, Metric learning as convex combinations of local models with generalization guarantees, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.82, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01323567

T. Zheng-jun-zha, M. Mei, Z. Wang, X. Wang, and . Hua, Robust distance metric learning with auxiliary knowledge, IJCAI, vol.49, p.57, 2009.

E. Zhong, W. Fan, Q. Yang, O. Verscheure, and J. Ren, Cross validation framework to choose amongst models and datasets for transfer learning, Proc. of European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), vol.6323, p.151, 2010.