Grassmannian Clustering for Multivariate Time Sequences

Beom Seok Oh, Beng Jin Teoh, Kar Ann Toh, Zhiping Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationNew Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers
EditorsChuan-Yu Chang, Chien-Chou Lin, Horng-Horng Lin
PublisherSpringer Verlag
Pages654-664
Number of pages11
ISBN (Print)9789811391897
DOIs
Publication statusPublished - 2019 Jan 1
Event23rd International Computer Symposium, ICS 2018 - Yunlin, Taiwan, Province of China
Duration: 2018 Dec 202018 Dec 22

Publication series

NameCommunications in Computer and Information Science
Volume1013
ISSN (Print)1865-0929

Conference

Conference23rd International Computer Symposium, ICS 2018
CountryTaiwan, Province of China
CityYunlin
Period18/12/2018/12/22

Fingerprint

Grassmann Manifold
Grassmannian
Clustering algorithms
Clustering Algorithm
Clustering
Reproducing Kernel Hilbert Space
Hilbert spaces
Streamlines
Gesture
Diffeomorphisms
Comparative Study
State Space
kernel
Motion
Modeling
Human

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

Cite this

Oh, B. S., Teoh, B. J., Toh, K. A., & Lin, Z. (2019). Grassmannian Clustering for Multivariate Time Sequences. In C-Y. Chang, C-C. Lin, & H-H. Lin (Eds.), New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers (pp. 654-664). (Communications in Computer and Information Science; Vol. 1013). Springer Verlag. https://doi.org/10.1007/978-981-13-9190-3_72
Oh, Beom Seok ; Teoh, Beng Jin ; Toh, Kar Ann ; Lin, Zhiping. / Grassmannian Clustering for Multivariate Time Sequences. New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. editor / Chuan-Yu Chang ; Chien-Chou Lin ; Horng-Horng Lin. Springer Verlag, 2019. pp. 654-664 (Communications in Computer and Information Science).
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Oh, BS, Teoh, BJ, Toh, KA & Lin, Z 2019, Grassmannian Clustering for Multivariate Time Sequences. in C-Y Chang, C-C Lin & H-H Lin (eds), New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. Communications in Computer and Information Science, vol. 1013, Springer Verlag, pp. 654-664, 23rd International Computer Symposium, ICS 2018, Yunlin, Taiwan, Province of China, 18/12/20. https://doi.org/10.1007/978-981-13-9190-3_72

Grassmannian Clustering for Multivariate Time Sequences. / Oh, Beom Seok; Teoh, Beng Jin; Toh, Kar Ann; Lin, Zhiping.

New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. ed. / Chuan-Yu Chang; Chien-Chou Lin; Horng-Horng Lin. Springer Verlag, 2019. p. 654-664 (Communications in Computer and Information Science; Vol. 1013).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Oh BS, Teoh BJ, Toh KA, Lin Z. Grassmannian Clustering for Multivariate Time Sequences. In Chang C-Y, Lin C-C, Lin H-H, editors, New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. Springer Verlag. 2019. p. 654-664. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-13-9190-3_72