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2019 | OriginalPaper | Chapter

Grassmannian Clustering for Multivariate Time Sequences

Authors : Beom-Seok Oh, Andrew Beng Jin Teoh, Kar-Ann Toh, Zhiping Lin

Published in: New Trends in Computer Technologies and Applications

Publisher: Springer Singapore

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Abstract

In this paper, we streamline the Grassmann multivariate time sequence (MTS) clustering for state-space dynamical modelling into three umbrella approaches: (i) Intrinsic approach where clustering is entirely constrained within the manifold, (ii) Extrinsic approach where Grassmann manifold is flattened via local diffeomorphisms or embedded into Reproducing Kernel Hilbert Spaces via Grassmann kernels, (iii) Semi-intrinsic approach where clustering algorithm is performed on Grassmann manifolds via Karcher mean. Consequently, 11 Grassmann clustering algorithms are derived and demonstrated through a comprehensive comparative study on human motion gesture derived MTS data.

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Metadata
Title
Grassmannian Clustering for Multivariate Time Sequences
Authors
Beom-Seok Oh
Andrew Beng Jin Teoh
Kar-Ann Toh
Zhiping Lin
Copyright Year
2019
Publisher
Springer Singapore
Book
New Trends in Computer Technologies and Applications

Print ISBN: 978-981-13-9189-7

Electronic ISBN: 978-981-13-9190-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-13-9190-3_72

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