Feature separability based on the distance matrix

Yu Zhu; Jinqiu Sun; Min Wang; Rui Yao; Yanning Zhang

doi:10.1109/ICOT.2017.8336087

Feature separability based on the distance matrix

Yu Zhu
, Jinqiu Sun
, Min Wang
, Rui Yao
, Yanning Zhang

School of Computer Science

Northwestern Polytechnical University Xian
University of Mining and Technology

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

1 Scopus citations

Abstract

Feature extraction is a key step in the classification and recognition problem. Features from different methods vary a lot with different separability in their feature space. We propose a novel method based on the distance matrix to evaluate feature separability by describing the in-class aggregation and the between-class scatter of every class. Finally the separability of each feature class is measured individually. Experiments on the synthetic data and ORL face dataset prove its effectiveness and advantage with regard to the conventional methods.

Original language	English
Title of host publication	Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
Editors	Minghui Dong, Lei Wang, Yanfeng Lu, Haizhou Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	53-56
Number of pages	4
ISBN (Electronic)	9781538632758
DOIs	https://doi.org/10.1109/ICOT.2017.8336087
State	Published - 2 Jul 2017
Event	5th International Conference on Orange Technologies, ICOT 2017 - Singapore, Singapore Duration: 8 Dec 2017 → 10 Dec 2017

Publication series

Name	Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
Volume	2018-January

Conference

Conference	5th International Conference on Orange Technologies, ICOT 2017
Country/Territory	Singapore
City	Singapore
Period	8/12/17 → 10/12/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/ICOT.2017.8336087

Cite this

Zhu, Y., Sun, J., Wang, M., Yao, R., & Zhang, Y. (2017). Feature separability based on the distance matrix. In M. Dong, L. Wang, Y. Lu, & H. Li (Eds.), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017 (pp. 53-56). (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICOT.2017.8336087

Zhu, Yu ; Sun, Jinqiu ; Wang, Min et al. / Feature separability based on the distance matrix. Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. editor / Minghui Dong ; Lei Wang ; Yanfeng Lu ; Haizhou Li. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 53-56 (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017).

@inproceedings{31a4c90606b24e7da90bd8e1d1391081,

title = "Feature separability based on the distance matrix",

abstract = "Feature extraction is a key step in the classification and recognition problem. Features from different methods vary a lot with different separability in their feature space. We propose a novel method based on the distance matrix to evaluate feature separability by describing the in-class aggregation and the between-class scatter of every class. Finally the separability of each feature class is measured individually. Experiments on the synthetic data and ORL face dataset prove its effectiveness and advantage with regard to the conventional methods.",

author = "Yu Zhu and Jinqiu Sun and Min Wang and Rui Yao and Yanning Zhang",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th International Conference on Orange Technologies, ICOT 2017 ; Conference date: 08-12-2017 Through 10-12-2017",

year = "2017",

month = jul,

day = "2",

doi = "10.1109/ICOT.2017.8336087",

language = "英语",

series = "Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "53--56",

editor = "Minghui Dong and Lei Wang and Yanfeng Lu and Haizhou Li",

booktitle = "Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017",

}

Zhu, Y , Sun, J, Wang, M, Yao, R & Zhang, Y 2017, Feature separability based on the distance matrix. in M Dong, L Wang, Y Lu & H Li (eds), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 53-56, 5th International Conference on Orange Technologies, ICOT 2017, Singapore, Singapore, 8/12/17. https://doi.org/10.1109/ICOT.2017.8336087

Feature separability based on the distance matrix. / Zhu, Yu ; Sun, Jinqiu; Wang, Min et al.
Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. ed. / Minghui Dong; Lei Wang; Yanfeng Lu; Haizhou Li. Institute of Electrical and Electronics Engineers Inc., 2017. p. 53-56 (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017; Vol. 2018-January).

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

TY - GEN

T1 - Feature separability based on the distance matrix

AU - Zhu, Yu

AU - Sun, Jinqiu

AU - Wang, Min

AU - Yao, Rui

AU - Zhang, Yanning

PY - 2017/7/2

Y1 - 2017/7/2

N2 - Feature extraction is a key step in the classification and recognition problem. Features from different methods vary a lot with different separability in their feature space. We propose a novel method based on the distance matrix to evaluate feature separability by describing the in-class aggregation and the between-class scatter of every class. Finally the separability of each feature class is measured individually. Experiments on the synthetic data and ORL face dataset prove its effectiveness and advantage with regard to the conventional methods.

AB - Feature extraction is a key step in the classification and recognition problem. Features from different methods vary a lot with different separability in their feature space. We propose a novel method based on the distance matrix to evaluate feature separability by describing the in-class aggregation and the between-class scatter of every class. Finally the separability of each feature class is measured individually. Experiments on the synthetic data and ORL face dataset prove its effectiveness and advantage with regard to the conventional methods.

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M3 - 会议稿件

AN - SCOPUS:85048574831

T3 - Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017

SP - 53

EP - 56

BT - Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017

A2 - Dong, Minghui

A2 - Wang, Lei

A2 - Lu, Yanfeng

A2 - Li, Haizhou

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Orange Technologies, ICOT 2017

Y2 - 8 December 2017 through 10 December 2017

ER -

Zhu Y , Sun J, Wang M, Yao R, Zhang Y. Feature separability based on the distance matrix. In Dong M, Wang L, Lu Y, Li H, editors, Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 53-56. (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017). doi: 10.1109/ICOT.2017.8336087

Feature separability based on the distance matrix

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