Grassmannian Clustering for Multivariate Time Sequences

Beom Seok Oh; Andrew Beng Jin Teoh; Kar Ann Toh; Zhiping Lin

doi:10.1007/978-981-13-9190-3_72

Grassmannian Clustering for Multivariate Time Sequences

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

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

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 language	English
Title of host publication	New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers
Editors	Chuan-Yu Chang, Chien-Chou Lin, Horng-Horng Lin
Publisher	Springer Verlag
Pages	654-664
Number of pages	11
ISBN (Print)	9789811391897
DOIs	https://doi.org/10.1007/978-981-13-9190-3_72
Publication status	Published - 2019
Event	23rd International Computer Symposium, ICS 2018 - Yunlin, Taiwan, Province of China Duration: 2018 Dec 20 → 2018 Dec 22

Publication series

Name	Communications in Computer and Information Science
Volume	1013
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	23rd International Computer Symposium, ICS 2018
Country/Territory	Taiwan, Province of China
City	Yunlin
Period	18/12/20 → 18/12/22

Bibliographical note

Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.

All Science Journal Classification (ASJC) codes

Computer Science(all)
Mathematics(all)

Access to Document

10.1007/978-981-13-9190-3_72

Cite this

Oh, B. S., Teoh, A. 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, Andrew Beng Jin ; Toh, Kar Ann et al. / 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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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.",

author = "Oh, {Beom Seok} and Teoh, {Andrew Beng Jin} and Toh, {Kar Ann} and Zhiping Lin",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 23rd International Computer Symposium, ICS 2018 ; Conference date: 20-12-2018 Through 22-12-2018",

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Oh, BS, Teoh, ABJ , 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, Andrew Beng Jin ; Toh, Kar Ann et al.
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 proceeding › Conference contribution

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T1 - Grassmannian Clustering for Multivariate Time Sequences

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AU - Teoh, Andrew Beng Jin

AU - Toh, Kar Ann

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N2 - 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.

AB - 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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Oh BS, Teoh ABJ , 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). doi: 10.1007/978-981-13-9190-3_72

Grassmannian Clustering for Multivariate Time Sequences

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