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

Dept. of Applied Artificial Intelligence

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

2 Scopus citations

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
State	Published - 2019
Event	23rd International Computer Symposium, ICS 2018 - Yunlin, Taiwan, Province of China Duration: 20 Dec 2018 → 22 Dec 2018

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	20/12/18 → 22/12/18

Keywords

Clustering
Grassmann manifold
Mutivariate time sequence

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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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title = "Grassmannian Clustering for Multivariate Time Sequences",

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.",

keywords = "Clustering, Grassmann manifold, Mutivariate time sequence",

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",

year = "2019",

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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, 20/12/18. 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 › peer-review

TY - GEN

T1 - Grassmannian Clustering for Multivariate Time Sequences

AU - Oh, Beom Seok

AU - Teoh, Andrew Beng Jin

AU - Toh, Kar Ann

AU - Lin, Zhiping

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2019.

PY - 2019

Y1 - 2019

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.

KW - Clustering

KW - Grassmann manifold

KW - Mutivariate time sequence

UR - https://www.scopus.com/pages/publications/85069656755

U2 - 10.1007/978-981-13-9190-3_72

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

M3 - Conference contribution

AN - SCOPUS:85069656755

SN - 9789811391897

T3 - Communications in Computer and Information Science

SP - 654

EP - 664

BT - New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers

A2 - Chang, Chuan-Yu

A2 - Lin, Chien-Chou

A2 - Lin, Horng-Horng

PB - Springer Verlag

T2 - 23rd International Computer Symposium, ICS 2018

Y2 - 20 December 2018 through 22 December 2018

ER -

Oh BS, Teoh ABJ, Toh KA, Lin Z. Grassmannian Clustering for Multivariate Time Sequences. In Chang CY, Lin CC, Lin HH, 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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