Numerical methods for simultaneous diagonalization
SIAM Journal on Matrix Analysis and Applications
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Independent component analysis: algorithms and applications
Neural Networks
Learning mixtures of arbitrary gaussians
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
A Spectral Algorithm for Learning Mixtures of Distributions
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Learning linear transformations
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians
The Journal of Machine Learning Research
Learning a Parallelepiped: Cryptanalysis of GGH and NTRU Signatures
Journal of Cryptology
Efficiently learning mixtures of two Gaussians
Proceedings of the forty-second ACM symposium on Theory of computing
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Settling the Polynomial Learnability of Mixtures of Gaussians
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
Polynomial Learning of Distribution Families
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
A spectral algorithm for learning Hidden Markov Models
Journal of Computer and System Sciences
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This work provides a computationally efficient and statistically consistent moment-based estimator for mixtures of spherical Gaussians. Under the condition that component means are in general position, a simple spectral decomposition technique yields consistent parameter estimates from low-order observable moments, without additional minimum separation assumptions needed by previous computationally efficient estimation procedures. Thus computational and information-theoretic barriers to efficient estimation in mixture models are precluded when the mixture components have means in general position and spherical covariances. Some connections are made to estimation problems related to independent component analysis.