  Spectral methods of moments provide a powerful tool for learning the
parameters of latent variable models. Despite their theoretical appeal, the
applicability of these methods to real data is still limited due to a lack of
robustness to model misspecification. In this paper we present a hierarchical
approach to methods of moments to circumvent such limitations. Our method is
based on replacing the tensor decomposition step used in previous algorithms
with approximate joint diagonalization. Experiments on topic modeling show that
our method outperforms previous tensor decomposition methods in terms of speed
and model quality.
