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, R. , and L. , 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, D. , and F. , 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, C. , and X. , A Spectral Learning Algorithm for Finite State Transducers, Proceedings of ECML/PKDD (1), pp.156-171, 2011.
DOI : 10.1007/978-3-642-23780-5_20

B. Balle, A. Quattoni, C. , and X. , 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

S. B. Cohen, . Stratos, . Karl, . Collins, . Michael et al., Spectral learning of Latent- Variable PCFGs The Association for Computer Linguistics, ACL (1), pp.223-231, 2012.

F. Denis, M. Gybels, and A. Habrard, Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning (Long Version). arXiv:1312, 2013.

M. Droste, W. Kuich, and H. Vogler, Handbook of Weighted Automata, 2009.
DOI : 10.1007/978-3-642-01492-5

N. Halko, P. G. Martinsson, and J. A. Tropp, Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions, SIAM Review, vol.53, issue.2, pp.217-288, 2011.
DOI : 10.1137/090771806

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.

S. Kakade, Multivariate analysis, dimensionality reduction, and spectral methods, Lecture Notes (Matrix Concentration Derivations), 2010.

F. M. Luque, A. Quattoni, B. Balle, C. , and X. , 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.

S. Siddiqi, B. Boots, G. , and G. J. , 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.

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

S. Verwer, R. Eyraud, and C. 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