A Method of Moments for Mixture Models and Hidden Markov Models
暂无分享,去创建一个
[1] K. Pearson. Contributions to the Mathematical Theory of Evolution , 1894 .
[2] H. Hotelling. The most predictable criterion. , 1935 .
[3] Marcel Paul Schützenberger,et al. On the Definition of a Family of Automata , 1961, Inf. Control..
[4] R. Redner,et al. Mixture densities, maximum likelihood, and the EM algorithm , 1984 .
[5] A. F. Smith,et al. Statistical analysis of finite mixture distributions , 1986 .
[6] B. Lindsay. Moment Matrices: Applications in Mixtures , 1989 .
[7] V. N. Bogaevski,et al. Matrix Perturbation Theory , 1991 .
[8] David Yarowsky,et al. One Sense Per Discourse , 1992, HLT.
[9] B. Lindsay,et al. Multivariate Normal Mixtures: A Fast Consistent Method of Moments , 1993 .
[10] B. Lindsay. Mixture models : theory, geometry, and applications , 1995 .
[11] Joseph T. Chang,et al. Full reconstruction of Markov models on evolutionary trees: identifiability and consistency. , 1996, Mathematical biosciences.
[12] Alan M. Frieze,et al. Learning linear transformations , 1996, Proceedings of 37th Conference on Foundations of Computer Science.
[13] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[14] Herbert Jaeger,et al. Observable Operator Models for Discrete Stochastic Time Series , 2000, Neural Computation.
[15] Sanjeev Arora,et al. Learning mixtures of arbitrary gaussians , 2001, STOC '01.
[16] Frank McSherry,et al. Spectral partitioning of random graphs , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.
[17] 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..
[18] Rudolf Ahlswede,et al. Strong converse for identification via quantum channels , 2000, IEEE Trans. Inf. Theory.
[19] Sanjoy Dasgupta,et al. An elementary proof of a theorem of Johnson and Lindenstrauss , 2003, Random Struct. Algorithms.
[20] Dimitris Achlioptas,et al. On Spectral Learning of Mixtures of Distributions , 2005, COLT.
[21] Santosh S. Vempala,et al. The Spectral Method for General Mixture Models , 2005, COLT.
[22] Jon Feldman,et al. Learning mixtures of product distributions over discrete domains , 2005, FOCS.
[23] Elchanan Mossel,et al. Learning nonsingular phylogenies and hidden Markov models , 2005, Symposium on the Theory of Computing.
[24] Jon Feldman,et al. PAC Learning Mixtures of Axis-Aligned Gaussians with No Separation Assumption , 2006, ArXiv.
[25] Daniel Boley,et al. Vandermonde Factorization of a Hankel Matrix ? , 2006 .
[26] Sanjoy Dasgupta,et al. A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians , 2007, J. Mach. Learn. Res..
[27] Phong Q. Nguyen,et al. Learning a Parallelepiped: Cryptanalysis of GGH and NTRU Signatures , 2009, Journal of Cryptology.
[28] Santosh S. Vempala,et al. Isotropic PCA and Affine-Invariant Clustering , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.
[29] Christoph H. Lampert,et al. Correlational spectral clustering , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[30] Satish Rao,et al. Learning Mixtures of Product Distributions Using Correlations and Independence , 2008, COLT.
[31] Sham M. Kakade,et al. A spectral algorithm for learning Hidden Markov Models , 2008, J. Comput. Syst. Sci..
[32] Sham M. Kakade,et al. Multi-view clustering via canonical correlation analysis , 2009, ICML '09.
[33] Adam Tauman Kalai,et al. Efficiently learning mixtures of two Gaussians , 2010, STOC '10.
[34] Ankur Moitra,et al. Settling the Polynomial Learnability of Mixtures of Gaussians , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[35] Mikhail Belkin,et al. Polynomial Learning of Distribution Families , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[36] Benjamin Recht,et al. A Simpler Approach to Matrix Completion , 2009, J. Mach. Learn. Res..
[37] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.
[38] Nick Gravin,et al. The Inverse Moment Problem for Convex Polytopes , 2011, Discret. Comput. Geom..