Semi-Supervised feature selection with adaptive discriminant analysis

Weichan Zhong, Xiaojun Chen, Guowen Yuan, Yiqin Li, Feiping Nie

科研成果: 书/报告/会议事项章节会议稿件同行评审

4 引用 (Scopus)

摘要

In this paper, we propose a novel Adaptive Discriminant Analysis for semi-supervised feature selection, namely SADA. Instead of computing fixed similarities before performing feature selection, SADA simultaneously learns an adaptive similarity matrix S and a projection matrix W with an iterative method. In each iteration, S is computed from the projected distance with the learned W and W is computed with the learned S. Therefore, SADA can learn better projection matrix W by weakening the effect of noise features with the adaptive similarity matrix. Experimental results on 4 data sets show the superiority of SADA compared to 5 semi-supervised feature selection methods.

源语言英语
主期刊名33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
出版商AAAI press
10083-10084
页数2
ISBN(电子版)9781577358091
DOI
出版状态已出版 - 2019
活动33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 - Honolulu, 美国
期限: 27 1月 20191 2月 2019

出版系列

姓名33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019

会议

会议33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
国家/地区美国
Honolulu
时期27/01/191/02/19

指纹

探究 'Semi-Supervised feature selection with adaptive discriminant analysis' 的科研主题。它们共同构成独一无二的指纹。

引用此