Tensor decompositions for learning latent variable models

@article{Anandkumar2014TensorDF,
  title={Tensor decompositions for learning latent variable models},
  author={Anima Anandkumar and Rong Ge and Daniel J. Hsu and S. Kakade and Matus Telgarsky},
  journal={ArXiv},
  year={2014},
  volume={abs/1210.7559}
}
This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models--including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation--which exploits a certain tensor structure in their low-order observable moments (typically, of second- and third-order). Specifically, parameter estimation is reduced to the problem of extracting a certain (orthogonal) decomposition of a symmetric tensor derived from the… Expand
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