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|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.|
|Citation:||Hazan, Elad, Holden Lee, Karan Singh, Cyril Zhang, and Yi Zhang. "Spectral Filtering for General Linear Dynamical Systems." Advances in Neural Information Processing Systems 31 (2018).|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Advances in Neural Information Processing Systems|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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