Quinten Tupker

1paper

1 Paper

LGFeb 15, 2021
Online learning of Riemannian hidden Markov models in homogeneous Hadamard spaces

Quinten Tupker, Salem Said, Cyrus Mostajeran

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.