Learning Overcomplete Latent Variable Models through Tensor Methods
暂无分享,去创建一个
[1] E. Wigner. Characteristic Vectors of Bordered Matrices with Infinite Dimensions I , 1955 .
[2] J. Chang,et al. Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .
[3] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[4] 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.
[5] Terrence J. Sejnowski,et al. Learning Overcomplete Representations , 2000, Neural Computation.
[6] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[7] Gene H. Golub,et al. Rank-One Approximation to High Order Tensors , 2001, SIAM J. Matrix Anal. Appl..
[8] Demetri Terzopoulos,et al. Multilinear subspace analysis of image ensembles , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[9] M. Rudelson,et al. Lp-moments of random vectors via majorizing measures , 2005, math/0507023.
[10] R. Latala. Estimates of moments and tails of Gaussian chaoses , 2005, math/0505313.
[11] R. Latala. Estimates of moments and tails of Gaussian chaoses , 2005, math/0505313.
[12] Sanjoy Dasgupta,et al. A Concentration Theorem for Projections , 2006, UAI.
[13] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[14] Lieven De Lathauwer,et al. Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures , 2007, IEEE Transactions on Signal Processing.
[15] M. Rudelson,et al. The smallest singular value of a random rectangular matrix , 2008, 0802.3956.
[16] Pierre Comon,et al. Handbook of Blind Source Separation: Independent Component Analysis and Applications , 2010 .
[17] Trac D. Tran,et al. Tensor sparsification via a bound on the spectral norm of random tensors , 2010, ArXiv.
[18] Quoc V. Le,et al. ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning , 2011, NIPS.
[19] Honglak Lee,et al. An Analysis of Single-Layer Networks in Unsupervised Feature Learning , 2011, AISTATS.
[20] Joel A. Tropp,et al. User-Friendly Tail Bounds for Sums of Random Matrices , 2010, Found. Comput. Math..
[21] R. Adamczak,et al. Chevet type inequality and norms of submatrices , 2011, 1107.4066.
[22] Anima Anandkumar,et al. Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation , 2012, NIPS 2012.
[23] Anima Anandkumar,et al. A Method of Moments for Mixture Models and Hidden Markov Models , 2012, COLT.
[24] Pascal Vincent,et al. Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.
[25] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Anima Anandkumar,et al. A Tensor Spectral Approach to Learning Mixed Membership Community Models , 2013, COLT.
[27] Anima Anandkumar,et al. Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs , 2013, ArXiv.
[28] Sham M. Kakade,et al. Learning mixtures of spherical gaussians: moment methods and spectral decompositions , 2012, ITCS '13.
[29] Ryan P. Adams,et al. Contrastive Learning Using Spectral Methods , 2013, NIPS.
[30] D. L. Donoho,et al. Compressed sensing , 2006, IEEE Trans. Inf. Theory.
[31] Anima Anandkumar,et al. Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates , 2014, ArXiv.
[32] Aditya Bhaskara,et al. Smoothed analysis of tensor decompositions , 2013, STOC.
[33] Sanjeev Arora,et al. New Algorithms for Learning Incoherent and Overcomplete Dictionaries , 2013, COLT.
[34] Anima Anandkumar,et al. Tensor decompositions for learning latent variable models , 2012, J. Mach. Learn. Res..
[35] Le Song,et al. Nonparametric Estimation of Multi-View Latent Variable Models , 2013, ICML.
[36] Mikhail Belkin,et al. The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures , 2013, COLT.
[37] Santosh S. Vempala,et al. Fourier PCA and robust tensor decomposition , 2013, STOC.
[38] Anima Anandkumar,et al. When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity , 2013, J. Mach. Learn. Res..
[39] Anima Anandkumar,et al. Online tensor methods for learning latent variable models , 2013, J. Mach. Learn. Res..
[40] David Steurer,et al. Dictionary Learning and Tensor Decomposition via the Sum-of-Squares Method , 2014, STOC.
[41] Prateek Jain,et al. Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization , 2013, SIAM J. Optim..