Online learning of Riemannian hidden Markov models in homogeneous Hadamard spaces

DOI10.1007/978-3-030-80209-7_5zbMath1487.60138arXiv2102.07771OpenAlexW3186543691MaRDI QIDQ2117823

Quinten Tupker, Cyrus Mostajeran, Salem Said

Publication date: 22 March 2022

Full work available at URL: https://arxiv.org/abs/2102.07771

zbMATH Keywords

hidden Markov modelRiemannian manifoldGaussian distributionstochastic gradient descentexpectation-maximization\(k\)-means clustering

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Uses Software

• Matrix Means Toolbox

Cites Work

• Computing the Karcher mean of symmetric positive definite matrices
• A Riemannian framework for tensor computing
• The Kähler Mean of Block-Toeplitz Matrices with Toeplitz Structured Blocks
• Baum's forward-backward algorithm revisited
• On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure
• Riemannian Gaussian Distributions on the Space of Symmetric Positive Definite Matrices
• Exact equivalences and phase discrepancies between random matrix ensembles