Multivariate Time Series Clustering with State Space Dynamical Modeling and Grassmann Manifold Learning: A Systematic Review on Human Motion Data

Multivariate Time Series Clustering with State Space Dynamical Modeling and Grassmann Manifold Learning: A Systematic Review on Human Motion Data

Multivariate time series (MTS) clustering has been an essential research topic in various domains over the past decades. However, inherent properties of MTS data—namely, temporal dynamics and inter-variable correlations—make MTS clustering challenging. These challenges can be addressed in Grassmann...

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Bibliographic Details
Main Authors: Sebin Heo, Andrew Beng Jin Teoh, Sunjin Yu, Beom-Seok Oh
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
clustering
geodesic distance
Grassmann manifold
Karcher mean
kernel method
multivariate time series
Online Access:https://www.mdpi.com/2076-3417/15/1/43
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