Fetching the paper…
Reading the bibliography…
We provide guarantees for learning latent variable models emphasizing on the overcomplete regime, where the dimensionality of the latent space can exceed the observed dimensionality.
Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition
J Douglas Carroll and Jih-Jie Chang · 1970
Earlier work this paper cites.
Independent component analysis, a new concept?
P. Comon · 1994
Earlier work this paper cites.
Independent component analysis, a survey of some algebraic methods
J. F. Cardoso and Pierre Comon · 1996
Earlier work this paper cites.
Independent component analysis: algorithms and applications
A. Hyvarinen and E. Oja · 2000
Earlier work this paper cites.
Learning overcomplete representations
M. S. Lewicki and T. J. Sejnowski · 2000
Earlier work this paper cites.
Near-optimal signal recovery from random projections: Universal encoding strategies?
Emmanuel J Candes and Terence Tao · 2006
Earlier work this paper cites.
Compressed sensing
D. Donoho · 2006
Earlier work this paper cites.
Estimates of moments and tails of Gaussian chaoses
R. Latala · 2006
Earlier work this paper cites.
Fourth-order cumulant-based blind identification of underdetermined mixtures
L. De Lathauwer, J. Castaing, and J.-F. Cardoso · 2007
Cited alongside, same era.
The smallest singular value of a random rectangular matrix
M. Rudelson and R. Vershynin · 2009
Cited alongside, same era.
Handbook of Blind Source Separation: Independent Component Analysis and Applications
P. Comon and C. Jutten · 2010
Cited alongside, same era.
Tensor sparsification via a bound on the spectral norm of random tensors
N. H. Nguyen, P. Drineas, and T. D. Tran · 2010
Cited alongside, same era.
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning
Q. V. Le, A. Karpenko, J. Ngiam, and A. Y. Ng · 2011
Cited alongside, same era.
Unsupervised feature learning and deep learning: A review and new perspectives
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
A. Agarwal, A. Anandkumar, P. Jain, P. Netrapalli, and R. Tandon · 2013
Later among the works it cites.
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures
J. Anderson, M. Belkin, N. Goyal, L. Rademacher, and J. Voss · 2013
Later among the works it cites.
New Algorithms for Learning Incoherent and Overcomplete Dictionaries
S. Arora, R. Ge, and A. Moitra · 2013
Later among the works it cites.
Smoothed analysis of tensor decompositions
A. Bhaskara, M. Charikar, A. Moitra, and A. Vijayaraghavan · 2013
Later among the works it cites.
N. Goyal, S. Vempala, and Y. Xiao · 2013
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Bengio, A. Courville, and P. Vincent · 2012
Cited alongside, same era.
Learning Mixtures of Spherical Gaussians: Moment Methods and Spectral Decompositions
D. Hsu and S. M. Kakade · 2012
Cited alongside, same era.
User-friendly tail bounds for sums of random matrices
Joel A. Tropp · 2012
Cited alongside, same era.
Tensor Methods for Learning Latent Variable Models
A. Anandkumar, R. Ge, D. Hsu, S. M. Kakade, and M. Telgarsky
Cited in the paper.
A Method of Moments for Mixture Models and Hidden Markov Models
A. Anandkumar, D. Hsu, and S. M. Kakade
Cited in the paper.
Two SVDs Suffice: Spectral Decompositions for Probabilistic Topic Modeling and Latent Dirichlet Allocation
A. Anandkumar, D. P. Foster, D. Hsu, S. M. Kakade, and Y. K. Liu
Cited in the paper.
A Tensor Spectral Approach to Learning Mixed Membership Community Models
A. Anandkumar, R. Ge, D. Hsu, and S. M. Kakade
Cited in the paper.
Later among the works it cites.
Nonparametric estimation of multi-view latent variable models
L. Song, A. Anandkumar, B. Dai, and B. Xie · 2013
Later among the works it cites.
Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank- 1 1 Updates
Anima Anandkumar, Rong Ge, and Majid Janzamin · 2014
Closest in time.
Dictionary learning and tensor decomposition via the sum-of-squares method
Boaz Barak, Jonathan Kelner, and David Steurer · 2014
Closest in time.