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Tensor decompositions for learning latent variable models (2014)
| Citations: | 83 - 7 self |
BibTeX
@MISC{Anandkumar14tensordecompositions,
author = {Animashree Anandkumar and Rong Ge and Daniel Hsu and Sham M. Kakade and Matus Telgarsky},
title = {Tensor decompositions for learning latent variable models },
year = {2014}
}
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Abstract
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 (orthog-onal) decomposition of a symmetric tensor derived from the moments; this decomposition can be viewed as a natural generalization of the singular value decomposition for matrices. Although tensor decompositions are generally intractable to compute, the decomposition of these specially structured tensors can be efficiently obtained by a variety of approaches, including power iterations and maximization approaches (similar to the case of matrices). A detailed analysis of a robust tensor power method is provided, establishing an analogue of Wedin’s perturbation theorem for the singular vectors of matrices. This implies a ro-bust and computationally tractable estimation approach for several popular latent variable models.
Keyphrases
latent variable model tensor decomposition arxiv preprint arxiv tractable estimation approach power iteration singular vector singular value decomposition certain tensor structure wedin perturbation theorem robust tensor power method several popular latent variable model latent dirichlet allocation maximization approach efficient parameter estimation method wide class low-order observable moment hidden markov model symmetric tensor gaussian mixture model natural generalization detailed analysis structured tensor
