R. Ahlswede and A. Winter, Addendum to "Strong converse for identification via quantum channels", IEEE Transactions on Information Theory, vol.49, issue.1, pp.569-579, 2002.
DOI : 10.1109/TIT.2002.806161

A. Anandkumar, D. P. Foster, D. Hsu, S. Kakade, and Y. Liu, A Spectral Algorithm for Latent Dirichlet Allocation, Proceedings of NIPS, pp.926-934, 2012.
DOI : 10.1007/s00453-014-9909-1

A. Anandkumar, D. Hsu, F. Huang, and S. Kakade, Learning mixtures of tree graphical models, Proceedings of NIPS, pp.1061-1069, 2012.

A. Anandkumar, D. Hsu, and S. M. Kakade, A method of moments for mixture models and hidden markov models, Proceedings of COLT -Journal of Machine Learning Research - Proceedings Track, pp.33-34, 2012.

R. Bailly, Méthodes spectrales pour l'inférence grammaticale probabiliste de langages stochastiques rationnels, 2011.

R. Bailly, F. Denis, and L. Ralaivola, Grammatical inference as a principal component analysis problem, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, p.5, 2009.
DOI : 10.1145/1553374.1553379

R. Bailly, A. Habrard, and F. Denis, A Spectral Approach for Probabilistic Grammatical Inference on Trees, Proceedings of ALT, pp.74-88, 2010.
DOI : 10.1007/978-3-642-16108-7_10

URL : https://hal.archives-ouvertes.fr/hal-00607096

B. Balle and M. Mohri, Spectral learning of general weighted automata via constrained matrix completion, Proceedings of NIPS, pp.2168-2176, 2012.

B. Balle, A. Quattoni, and X. Carreras, A Spectral Learning Algorithm for Finite State Transducers, Proceedings of ECML/PKDD, pp.156-171, 2011.
DOI : 10.1007/978-3-642-23780-5_20

B. Balle, A. Quattoni, and X. Carreras, Local loss optimization in operator models: A new insight into spectral learning, Proceedings of ICML, 2012.

B. Balle, X. Carreras, F. M. Luque, and A. Quattoni, Spectral learning of weighted automata, Machine Learning, 2013.
DOI : 10.1007/s10994-013-5416-x

B. Balle, W. L. Hamilton, and J. Pineau, Methods of moments for learning stochastic languages: Unified presentation and empirical comparison, Proceedings of ICML, 2014.

J. Berstel and C. Reutenauer, Rational series and their languages. EATCS monographs on theoretical computer science, 1988.
URL : https://hal.archives-ouvertes.fr/hal-00619791

Z. Bo, On the spectral radius of nonnegative matrices, Australasian Journal of Combinatorics, vol.22, pp.301-306, 2000.

S. B. Cohen, K. Stratos, M. Collins, D. P. Foster, and L. H. Ungar, Spectral learning of Latent-Variable PCFGs The Association for Computer Linguistics, ACL (1), pp.223-231, 2012.

D. Hsu, S. M. Kakade, and T. Zhang, A spectral algorithm for learning Hidden Markov Models, Proceedings of COLT, 2009.
DOI : 10.1016/j.jcss.2011.12.025

D. Hsu, S. M. Kakade, and T. Zhang, Dimension-free tail inequalities for sums of random matrices. ArXiv e-prints, 2011.

D. Hsu, S. Kakade, and T. Zhang, Tail inequalities for sums of random matrices that depend on the intrinsic dimension, Electronic Communications in Probability, vol.17, issue.0, pp.1-13, 2012.
DOI : 10.1214/ECP.v17-1869

F. M. Luque, A. Quattoni, B. Balle, and X. Carreras, Spectral learning for non-deterministic dependency parsing, Proceedings of EACL, pp.409-419, 2012.

A. P. Parikh, L. Song, and E. P. Xing, A spectral algorithm for latent tree graphical models, Proceedings of ICML, pp.1065-1072, 2011.

A. Salomaa and M. Soittola, Automata-theoretic aspects of formal power series. Texts and monographs in computer science, 1978.

S. Siddiqi, B. Boots, and G. J. Gordon, Reduced-rank hidden Markov models, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-2010), 2010.

L. Song, B. Boots, S. M. Siddiqi, G. J. Gordon, and A. J. Smola, Hilbert space embeddings of hidden markov models, Proceedings of ICML, pp.991-998, 2010.

E. M. Stein and R. Shakarchi, Real analysis : measure theory, integration, and Hilbert spaces Princeton lectures in analysis, 2005.

G. W. Stewart, Perturbation theory for the singular value decomposition, SVD and Signal Processing II: Algorithms, Analysis and Applications, pp.99-109, 1990.

J. A. Tropp, User-Friendly Tail Bounds for Sums of Random Matrices, Foundations of Computational Mathematics, vol.16, issue.2, pp.389-434, 2012.
DOI : 10.1007/s10208-011-9099-z

R. Vershynin, Compressed Sensing, chapter 5 Introduction to the non-asymptotic analysis of random matrices, pp.210-268, 2012.

S. Verwer, R. Eyraud, C. De, and . Higuera, Results of the PAutomaC probabilistic automaton learning competition, Journal of Machine Learning Research -Proceedings Track, vol.21, pp.243-248, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00833419