Spectral Filtering For General Linear Dynamical Systems

Authors:
Elad Hazan Princeton University
HOLDEN LEE Princeton
Karan Singh Princeton University
Cyril Zhang Princeton University
Yi Zhang Princeton

Introduction:

The authors 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.

Abstract:

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.

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