A segment-wise extraction of multivariate time-series features for Grassmann clustering

Sebin Heo; Bezawit Habtamu Nuriye; Beomseok Oh

doi:10.1109/ICEIC57457.2023.10049970

A segment-wise extraction of multivariate time-series features for Grassmann clustering

Sebin Heo, Bezawit Habtamu Nuriye, Beomseok Oh

Dept. of Applied Artificial Intelligence

Gyeongsang National University

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

Abstract

In this paper, a novel approach of extracting features from multivariate time-series (MTS) with different time lengths, is proposed to enhance the clustering accuracy. Particularly, the feature extraction is conducted on time-sample segments of MTS, in which several segments are defined without overlapping. As for feature extractor, the conventional two-dimensional principal component analysis (2DPCA) is deployed due to its proven effectiveness in feature representation. Our experimental results show that the proposed segment-wise extraction of 2DPCA features is helpful in enhancing the clustering accuracy.

Original language	English
Title of host publication	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350320213
DOIs	https://doi.org/10.1109/ICEIC57457.2023.10049970
State	Published - 2023
Event	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 - Singapore, Singapore Duration: 5 Feb 2023 → 8 Feb 2023

Publication series

Name	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

Conference

Conference	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
Country/Territory	Singapore
City	Singapore
Period	5/02/23 → 8/02/23

Keywords

Clustering
Feature Extraction
Grassmann Manifold
Multivariate Time-Series

Access to Document

10.1109/ICEIC57457.2023.10049970

Cite this

Heo, S., Nuriye, B. H., & Oh, B. (2023). A segment-wise extraction of multivariate time-series features for Grassmann clustering. In 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 (2023 International Conference on Electronics, Information, and Communication, ICEIC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEIC57457.2023.10049970

Heo, Sebin ; Nuriye, Bezawit Habtamu ; Oh, Beomseok. / A segment-wise extraction of multivariate time-series features for Grassmann clustering. 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Conference on Electronics, Information, and Communication, ICEIC 2023).

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title = "A segment-wise extraction of multivariate time-series features for Grassmann clustering",

abstract = "In this paper, a novel approach of extracting features from multivariate time-series (MTS) with different time lengths, is proposed to enhance the clustering accuracy. Particularly, the feature extraction is conducted on time-sample segments of MTS, in which several segments are defined without overlapping. As for feature extractor, the conventional two-dimensional principal component analysis (2DPCA) is deployed due to its proven effectiveness in feature representation. Our experimental results show that the proposed segment-wise extraction of 2DPCA features is helpful in enhancing the clustering accuracy.",

keywords = "Clustering, Feature Extraction, Grassmann Manifold, Multivariate Time-Series",

author = "Sebin Heo and Nuriye, \{Bezawit Habtamu\} and Beomseok Oh",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 ; Conference date: 05-02-2023 Through 08-02-2023",

year = "2023",

doi = "10.1109/ICEIC57457.2023.10049970",

language = "English",

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Heo, S, Nuriye, BH & Oh, B 2023, A segment-wise extraction of multivariate time-series features for Grassmann clustering. in 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023. 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023, Singapore, Singapore, 5/02/23. https://doi.org/10.1109/ICEIC57457.2023.10049970

A segment-wise extraction of multivariate time-series features for Grassmann clustering. / Heo, Sebin; Nuriye, Bezawit Habtamu; Oh, Beomseok.
2023 International Conference on Electronics, Information, and Communication, ICEIC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Conference on Electronics, Information, and Communication, ICEIC 2023).

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

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AU - Oh, Beomseok

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Y1 - 2023

N2 - In this paper, a novel approach of extracting features from multivariate time-series (MTS) with different time lengths, is proposed to enhance the clustering accuracy. Particularly, the feature extraction is conducted on time-sample segments of MTS, in which several segments are defined without overlapping. As for feature extractor, the conventional two-dimensional principal component analysis (2DPCA) is deployed due to its proven effectiveness in feature representation. Our experimental results show that the proposed segment-wise extraction of 2DPCA features is helpful in enhancing the clustering accuracy.

AB - In this paper, a novel approach of extracting features from multivariate time-series (MTS) with different time lengths, is proposed to enhance the clustering accuracy. Particularly, the feature extraction is conducted on time-sample segments of MTS, in which several segments are defined without overlapping. As for feature extractor, the conventional two-dimensional principal component analysis (2DPCA) is deployed due to its proven effectiveness in feature representation. Our experimental results show that the proposed segment-wise extraction of 2DPCA features is helpful in enhancing the clustering accuracy.

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M3 - Conference contribution

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T3 - 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

BT - 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 5 February 2023 through 8 February 2023

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Heo S, Nuriye BH, Oh B. A segment-wise extraction of multivariate time-series features for Grassmann clustering. In 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 International Conference on Electronics, Information, and Communication, ICEIC 2023). doi: 10.1109/ICEIC57457.2023.10049970

A segment-wise extraction of multivariate time-series features for Grassmann clustering

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