Analyzing human behavior in subspace: Dimensionality reduction + classification

Yang Liu; Zhonglei Gu; Tobey H. Ko

Analyzing human behavior in subspace: Dimensionality reduction + classification

Yang Liu, Zhonglei Gu, Tobey H. Ko

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

Automated detection of human behavior in a social setting has drawn considerable interests in recent years. In this working notes paper, we describe our system developed for human behavior analysis. The system is composed of two components: 1) a dimensionality reduction module that maps the original data to a subspace; and 2) a classifier module that classifies the test data based on the labels of training data in the learned subspace. The developed system is evaluated on the MediaEval 2018 Human Behavior Analysis Task.

Original language	English
Title of host publication	Working Notes Proceedings of the MediaEval 2018 Workshop
Editors	Martha Larson, Piyush Arora, Claire-Hélène Demarty, Michael Riegler, Benjamin Bischke, Emmanuel Dellandrea, Mathias Lux, Alastair Porter, Gareth J. F. Jones
Publisher	CEUR-WS
Number of pages	3
Publication status	Published - Oct 2018
Event	MediaEval 2018: Multimedia Benchmark Workshop - Sophia Antipolis, France Duration: 29 Oct 2018 → 31 Oct 2018 https://ceur-ws.org/Vol-2283/

Publication series

Name	CEUR Workshop Proceedings
Publisher	CEUR-WS
Volume	2283
ISSN (Print)	1613-0073

Conference

Conference	MediaEval 2018: Multimedia Benchmark Workshop
Country/Territory	France
City	Sophia Antipolis
Period	29/10/18 → 31/10/18
Internet address	https://ceur-ws.org/Vol-2283/

Scopus Subject Areas

Computer Science(all)

Access to Document

http://ceur-ws.org/Vol-2283/MediaEval_18_paper_57.pdf

Cite this

Liu, Y., Gu, Z., & Ko, T. H. (2018). Analyzing human behavior in subspace: Dimensionality reduction + classification. In M. Larson, P. Arora, C.-H. Demarty, M. Riegler, B. Bischke, E. Dellandrea, M. Lux, A. Porter, & G. J. F. Jones (Eds.), Working Notes Proceedings of the MediaEval 2018 Workshop (CEUR Workshop Proceedings; Vol. 2283). CEUR-WS. http://ceur-ws.org/Vol-2283/MediaEval_18_paper_57.pdf

Liu, Yang ; Gu, Zhonglei ; Ko, Tobey H. / Analyzing human behavior in subspace : Dimensionality reduction + classification. Working Notes Proceedings of the MediaEval 2018 Workshop. editor / Martha Larson ; Piyush Arora ; Claire-Hélène Demarty ; Michael Riegler ; Benjamin Bischke ; Emmanuel Dellandrea ; Mathias Lux ; Alastair Porter ; Gareth J. F. Jones. CEUR-WS, 2018. (CEUR Workshop Proceedings).

@inproceedings{24134680216c47a0a46a2db810841bb6,

title = "Analyzing human behavior in subspace: Dimensionality reduction + classification",

abstract = "Automated detection of human behavior in a social setting has drawn considerable interests in recent years. In this working notes paper, we describe our system developed for human behavior analysis. The system is composed of two components: 1) a dimensionality reduction module that maps the original data to a subspace; and 2) a classifier module that classifies the test data based on the labels of training data in the learned subspace. The developed system is evaluated on the MediaEval 2018 Human Behavior Analysis Task.",

author = "Yang Liu and Zhonglei Gu and Ko, {Tobey H.}",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61503317, in part by the General Research Fund (GRF) from the Research Grant Council (RGC) of Hong Kong SAR under Project HKBU12202417, and in part by the SZSTI Grant with the Projct Code JCYJ20170307161544087.; MediaEval 2018: Multimedia Benchmark Workshop ; Conference date: 29-10-2018 Through 31-10-2018",

year = "2018",

month = oct,

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS",

editor = "Martha Larson and Piyush Arora and Claire-H{\'e}l{\`e}ne Demarty and Michael Riegler and Benjamin Bischke and Emmanuel Dellandrea and Mathias Lux and Alastair Porter and Jones, {Gareth J. F.}",

booktitle = "Working Notes Proceedings of the MediaEval 2018 Workshop",

url = "https://ceur-ws.org/Vol-2283/",

}

Liu, Y, Gu, Z & Ko, TH 2018, Analyzing human behavior in subspace: Dimensionality reduction + classification. in M Larson, P Arora, C-H Demarty, M Riegler, B Bischke, E Dellandrea, M Lux, A Porter & GJF Jones (eds), Working Notes Proceedings of the MediaEval 2018 Workshop. CEUR Workshop Proceedings, vol. 2283, CEUR-WS, MediaEval 2018: Multimedia Benchmark Workshop, Sophia Antipolis, France, 29/10/18. <http://ceur-ws.org/Vol-2283/MediaEval_18_paper_57.pdf>

Analyzing human behavior in subspace: Dimensionality reduction + classification. / Liu, Yang; Gu, Zhonglei; Ko, Tobey H.
Working Notes Proceedings of the MediaEval 2018 Workshop. ed. / Martha Larson; Piyush Arora; Claire-Hélène Demarty; Michael Riegler; Benjamin Bischke; Emmanuel Dellandrea; Mathias Lux; Alastair Porter; Gareth J. F. Jones. CEUR-WS, 2018. (CEUR Workshop Proceedings; Vol. 2283).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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N1 - Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61503317, in part by the General Research Fund (GRF) from the Research Grant Council (RGC) of Hong Kong SAR under Project HKBU12202417, and in part by the SZSTI Grant with the Projct Code JCYJ20170307161544087.

PY - 2018/10

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N2 - Automated detection of human behavior in a social setting has drawn considerable interests in recent years. In this working notes paper, we describe our system developed for human behavior analysis. The system is composed of two components: 1) a dimensionality reduction module that maps the original data to a subspace; and 2) a classifier module that classifies the test data based on the labels of training data in the learned subspace. The developed system is evaluated on the MediaEval 2018 Human Behavior Analysis Task.

AB - Automated detection of human behavior in a social setting has drawn considerable interests in recent years. In this working notes paper, we describe our system developed for human behavior analysis. The system is composed of two components: 1) a dimensionality reduction module that maps the original data to a subspace; and 2) a classifier module that classifies the test data based on the labels of training data in the learned subspace. The developed system is evaluated on the MediaEval 2018 Human Behavior Analysis Task.

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A2 - Demarty, Claire-Hélène

A2 - Riegler, Michael

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PB - CEUR-WS

Y2 - 29 October 2018 through 31 October 2018

ER -

Analyzing human behavior in subspace: Dimensionality reduction + classification

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