  We give a polynomial-time algorithm for learning latent-state linear
dynamical systems without system identification, and without assumptions on the
spectral radius of the system's transition matrix. The algorithm extends the
recently introduced technique of spectral filtering, previously applied only to
systems with a symmetric transition matrix, using a novel convex relaxation to
allow for the efficient identification of phases.
