mlsanityData to 7 Oct 2026

Online learning of Riemannian hidden Markov models in homogeneous Hadamard spaces

arXiv 2102.07771 ↗First posted 15 Feb 2021cs.LG math.DG stat.COcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 1 of its 16 references that are in the map. First posted on arXiv 15 Feb 2021.

Builds on

1 of 16 references.

References · 1

  1. 2020Riemannian Gaussian distributions, random matrix ensembles and diffusion kernelscited by 3

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Cited by year

Citing papers per year: 2022: 2; 2023: 1.

3 papers.

  1. 2023Differential geometry with extreme eigenvalues in the positive semidefinite conecited by 5
  2. 2022Geometric Learning of Hidden Markov Models via a Method of Moments Algorithmcited by 2
  3. 2022Riemannian statistics meets random matrix theory: towards learning from high-dimensional covariance matricescited by 0