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Online Learning of Riemannian Hidden Markov Models in Homogeneous Hadamard Spaces

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Geometric Science of Information (GSI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12829))

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Abstract

Hidden Markov models with observations in a Euclidean space play an important role in signal and image processing. Previous work extending to models where observations lie in Riemannian manifolds based on the Baum-Welch algorithm suffered from high memory usage and slow speed. Here we present an algorithm that is online, more accurate, and offers dramatic improvements in speed and efficiency.

This work benefited from partial support by the European Research Council under the Advanced ERC Grant Agreement Switchlet n. 670645. Q.T. also received partial funding from the Cambridge Mathematics Placement (CMP) Programme. C.M. was supported by Fitzwilliam College and a Henslow Fellowship from the Cambridge Philosophical Society.

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Correspondence to Cyrus Mostajeran .

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Tupker, Q., Said, S., Mostajeran, C. (2021). Online Learning of Riemannian Hidden Markov Models in Homogeneous Hadamard Spaces. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2021. Lecture Notes in Computer Science(), vol 12829. Springer, Cham. https://doi.org/10.1007/978-3-030-80209-7_5

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  • DOI: https://doi.org/10.1007/978-3-030-80209-7_5

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