Local Loss Optimization in Operator Models: A New Insight into Spectral Learning

This paper revisits the spectral method for learning latent variable models defined in terms of observable operators. We give a new perspective on the method, showing that operators can be recovered by minimizing a loss defined on a finite subset of the domain. This leads to a derivation of a non-convex optimization similar to the spectral method. We also propose a regularized convex relaxation of this optimization. In practice our experiments show that a continuous regularization parameter (in contrast with the discrete number of states in the original method) allows a better tradeoff between accuracy and model complexity. We also prove that in general, a randomized strategy for choosing the local loss succeeds with high probability.