@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",
}
