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We present a novel analysis of the dynamics of tensor power iterations in the overcomplete regime where the tensor CP rank is larger than the input dimension.
Learning mixutres of gaussians
Sanjoy Dasgupta · 1999
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Learning overcomplete representations
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T. Zhang and G. Golub · 2001
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A spectral algorithm for learning mixtures of distributions
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Inequalities for spreads of matrix sums and products
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Learning mixtures of separated nonspherical gaussians
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Near-optimal signal recovery from random projections: Universal encoding strategies?
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Fourth-order cumulant-based blind identification of underdetermined mixtures
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Roger A. Horn and Charles R. Johnson · 2012
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A. Bhaskara, M. Charikar, A. Moitra, and A. Vijayaraghavan · 2013
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The number of eigenvalues of a tensor
Dustin Cartwright and Bernd Sturmfels · 2013
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N. Goyal, S. Vempala, and Y. Xiao · 2013
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Most tensor problems are NP hard
Christopher J. Hillar and Lek-Heng Lim · 2013
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Learning mixtures of spherical gaussians: moment methods and spectral decompositions
Daniel Hsu and Sham M Kakade · 2013
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Learning Overcomplete Latent Variable Models through Tensor Methods
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Learning Mixtures of Spherical Gaussians: Moment Methods and Spectral Decompositions
D. Hsu and S. M. Kakade · 2012
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Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank- 1 1 Updates
Anima Anandkumar, Rong Ge, and Majid Janzamin
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Sample Complexity Analysis for Learning Overcomplete Latent Variable Models through Tensor Methods
Anima Anandkumar, Rong Ge, and Majid Janzamin
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Tensor Decompositions for Learning Latent Variable Models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, and Matus Telgarsky
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Contributions to the mathematical theory of evolution
Karl Pearson
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A. Anandkumar, R. Ge, and M. Janzamin · 2015
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Decomposing overcomplete 3rd order tensors using sum-of-squares algorithms
Rong Ge and Tengyu Ma · 2015
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