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[1] K. Pearson. Contributions to the Mathematical Theory of Evolution , 1894 .
[2] F. L. Hitchcock. The Expression of a Tensor or a Polyadic as a Sum of Products , 1927 .
[3] F. L. Hitchcock. Multiple Invariants and Generalized Rank of a P‐Way Matrix or Tensor , 1928 .
[4] R. Cattell. “Parallel proportional profiles” and other principles for determining the choice of factors by rotation , 1944 .
[5] Marcel Paul Schützenberger,et al. On the Definition of a Family of Automata , 1961, Inf. Control..
[6] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[7] Richard A. Harshman,et al. Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .
[8] P. Wedin. Perturbation bounds in connection with singular value decomposition , 1972 .
[9] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[10] J. Kruskal. Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics , 1977 .
[11] R. Redner,et al. Mixture densities, maximum likelihood, and the EM algorithm , 1984 .
[12] L. L. Cam,et al. Asymptotic methods in statistical theory , 1986 .
[13] L. L. Cam,et al. Asymptotic Methods In Statistical Decision Theory , 1986 .
[14] P. McCullagh. Tensor Methods in Statistics , 1987 .
[15] Jean-Francois Cardoso,et al. Super-symmetric decomposition of the fourth-order cumulant tensor. Blind identification of more sources than sensors , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[16] A. Bunse-Gerstner,et al. Numerical Methods for Simultaneous Diagonalization , 1993, SIAM J. Matrix Anal. Appl..
[17] J. Cardoso,et al. Blind beamforming for non-gaussian signals , 1993 .
[18] S. Leurgans,et al. A Decomposition for Three-Way Arrays , 1993, SIAM J. Matrix Anal. Appl..
[19] Jean-Francois Cardoso,et al. Perturbation of joint diagonalizers , 1994 .
[20] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[21] Nathalie Delfosse,et al. Adaptive blind separation of independent sources: A deflation approach , 1995, Signal Process..
[22] B. Moor,et al. Subspace identification for linear systems , 1996 .
[23] Pierre Comon,et al. Independent component analysis, a survey of some algebraic methods , 1996, 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96.
[24] Joseph T. Chang,et al. Full reconstruction of Markov models on evolutionary trees: identifiability and consistency. , 1996, Mathematical biosciences.
[25] Alan M. Frieze,et al. Learning linear transformations , 1996, Proceedings of 37th Conference on Foundations of Computer Science.
[26] Robert M. Corless,et al. A reordered Schur factorization method for zero-dimensional polynomial systems with multiple roots , 1997, ISSAC.
[27] Stephen J. Wright,et al. Numerical Optimization , 2018, Fundamental Statistical Inference.
[28] Aapo Hyvärinen,et al. Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.
[29] Sanjoy Dasgupta,et al. Learning mixtures of Gaussians , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).
[30] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[31] Herbert Jaeger,et al. Observable Operator Models for Discrete Stochastic Time Series , 2000, Neural Computation.
[32] Joos Vandewalle,et al. On the Best Rank-1 and Rank-(R1 , R2, ... , RN) Approximation of Higher-Order Tensors , 2000, SIAM J. Matrix Anal. Appl..
[33] Sanjeev Arora,et al. Learning mixtures of arbitrary gaussians , 2001, STOC '01.
[34] Gene H. Golub,et al. Rank-One Approximation to High Order Tensors , 2001, SIAM J. Matrix Anal. Appl..
[35] Richard S. Sutton,et al. Predictive Representations of State , 2001, NIPS.
[36] Santosh S. Vempala,et al. A spectral algorithm for learning mixtures of distributions , 2002, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..
[37] Phillip A. Regalia,et al. On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors , 2001, SIAM J. Matrix Anal. Appl..
[38] Phillip A. Regalia,et al. Monotonic convergence of fixed-point algorithms for ICA , 2003, IEEE Trans. Neural Networks.
[39] Santosh S. Vempala,et al. A spectral algorithm for learning mixture models , 2004, J. Comput. Syst. Sci..
[40] L. Lathauwer,et al. On the Best Rank-1 and Rank-( , 2004 .
[41] Andreas Ziehe,et al. A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation , 2004, J. Mach. Learn. Res..
[42] Sanjeev Arora,et al. LEARNING MIXTURES OF SEPARATED NONSPHERICAL GAUSSIANS , 2005, math/0503457.
[43] Dimitris Achlioptas,et al. On Spectral Learning of Mixtures of Distributions , 2005, COLT.
[44] Elchanan Mossel,et al. Learning nonsingular phylogenies and hidden Markov models , 2005, STOC '05.
[45] Lek-Heng Lim,et al. Singular values and eigenvalues of tensors: a variational approach , 2005, 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005..
[46] M. Drton,et al. Algebraic factor analysis: tetrads, pentads and beyond , 2005, math/0509390.
[47] Liqun Qi,et al. Eigenvalues of a real supersymmetric tensor , 2005, J. Symb. Comput..
[48] L. Pachter,et al. Algebraic Statistics for Computational Biology: Preface , 2005 .
[49] Sébastien Roch,et al. A short proof that phylogenetic tree reconstruction by maximum likelihood is hard , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[50] Lieven De Lathauwer,et al. Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures , 2007, IEEE Transactions on Signal Processing.
[51] Sanjoy Dasgupta,et al. A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians , 2007, J. Mach. Learn. Res..
[52] Phong Q. Nguyen,et al. Learning a Parallelepiped: Cryptanalysis of GGH and NTRU Signatures , 2009, Journal of Cryptology.
[53] Santosh S. Vempala,et al. The Spectral Method for General Mixture Models , 2008, SIAM J. Comput..
[54] Santosh S. Vempala,et al. Isotropic PCA and Affine-Invariant Clustering , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.
[55] Gene H. Golub,et al. Symmetric Tensors and Symmetric Tensor Rank , 2008, SIAM J. Matrix Anal. Appl..
[56] Tim Austin. On exchangeable random variables and the statistics of large graphs and hypergraphs , 2008, 0801.1698.
[57] Satish Rao,et al. Learning Mixtures of Product Distributions Using Correlations and Independence , 2008, COLT.
[58] Sham M. Kakade,et al. A spectral algorithm for learning Hidden Markov Models , 2008, J. Comput. Syst. Sci..
[59] Shang-Hua Teng,et al. Smoothed analysis: an attempt to explain the behavior of algorithms in practice , 2009, CACM.
[60] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[61] Pierre Comon,et al. Subtracting a best rank-1 approximation may increase tensor rank , 2009, 2009 17th European Signal Processing Conference.
[62] Alper T. Erdogan,et al. On the Convergence of ICA Algorithms With Symmetric Orthogonalization , 2008, IEEE Transactions on Signal Processing.
[63] C. Matias,et al. Identifiability of parameters in latent structure models with many observed variables , 2008, 0809.5032.
[64] Byron Boots,et al. Closing the learning-planning loop with predictive state representations , 2009, Int. J. Robotics Res..
[65] Byron Boots,et al. Reduced-Rank Hidden Markov Models , 2009, AISTATS.
[66] Pierre Comon,et al. Handbook of Blind Source Separation: Independent Component Analysis and Applications , 2010 .
[67] Adam Tauman Kalai,et al. Efficiently learning mixtures of two Gaussians , 2010, STOC '10.
[68] Ankur Moitra,et al. Settling the Polynomial Learnability of Mixtures of Gaussians , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[69] Mikhail Belkin,et al. Polynomial Learning of Distribution Families , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[70] B. Sturmfels,et al. Binary Cumulant Varieties , 2011, 1103.0153.
[71] Le Song,et al. A Spectral Algorithm for Latent Tree Graphical Models , 2011, ICML.
[72] Robert H. Halstead,et al. Matrix Computations , 2011, Encyclopedia of Parallel Computing.
[73] Nathan Halko,et al. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..
[74] Raphaël Bailly. Quadratic Weighted Automata: Spectral Algorithm and Likelihood Maximization , 2011, ACML 2011.
[75] Tamara G. Kolda,et al. Shifted Power Method for Computing Tensor Eigenpairs , 2010, SIAM J. Matrix Anal. Appl..
[76] Byron Boots,et al. An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems , 2011, AAAI.
[77] Ariadna Quattoni,et al. Spectral Learning for Non-Deterministic Dependency Parsing , 2012, EACL.
[78] Mehryar Mohri,et al. Spectral Learning of General Weighted Automata via Constrained Matrix Completion , 2012, NIPS.
[79] Karl Stratos,et al. Spectral Learning of Latent-Variable PCFGs , 2012, ACL.
[80] Michael Collins,et al. Spectral Dependency Parsing with Latent Variables , 2012, EMNLP-CoNLL.
[81] Seungjin Choi,et al. Independent Component Analysis , 2009, Handbook of Natural Computing.
[82] Sanjeev Arora,et al. Learning Topic Models -- Going beyond SVD , 2012, 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science.
[83] Sham M. Kakade,et al. Identifiability and Unmixing of Latent Parse Trees , 2012, NIPS.
[84] Ariadna Quattoni,et al. Local Loss Optimization in Operator Models: A New Insight into Spectral Learning , 2012, ICML.
[85] Dean P. Foster,et al. Spectral dimensionality reduction for HMMs , 2012, ArXiv.
[86] Anima Anandkumar,et al. A Method of Moments for Mixture Models and Hidden Markov Models , 2012, COLT.
[87] Anima Anandkumar,et al. Learning Mixtures of Tree Graphical Models , 2012, NIPS.
[88] B. Sturmfels,et al. The number of eigenvalues of a tensor , 2010, 1004.4953.
[89] Sham M. Kakade,et al. Learning mixtures of spherical gaussians: moment methods and spectral decompositions , 2012, ITCS '13.
[90] Ryan P. Adams,et al. Contrastive Learning Using Spectral Methods , 2013, NIPS.
[91] Dean P. Foster,et al. Using Regression for Spectral Estimation of HMMs , 2013, SLSP.
[92] Christopher J. Hillar,et al. Most Tensor Problems Are NP-Hard , 2009, JACM.
[93] Aditya Bhaskara,et al. Smoothed analysis of tensor decompositions , 2013, STOC.
[94] Mikhail Belkin,et al. The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures , 2013, COLT.
[95] Anima Anandkumar,et al. A Spectral Algorithm for Latent Dirichlet Allocation , 2012, Algorithmica.
[96] Sanjeev Arora,et al. Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders , 2012, Algorithmica.