Discriminative latent-variable models are typically learned using EM or gradient-based optimization, which suer from local optima. In this paper, we develop a new computationally ecient and provably consistent estimator for a mixture of linear regressions, a simple instance of a discriminative latentvariable model. Our approach relies on a lowrank linear regression to recover a symmetric tensor, which can be factorized into the parameters using a tensor power method. We prove rates of convergence for our estimator and provide an empirical evaluation illustrating its strengths relative to local optimization (EM).
['Arun Tejasvi Chaganty', 'Percy Liang']
Spectral Experts for Estimating Mixtures of Linear Regressions