文献类型：期刊文献

英文题名：Regularized discriminant entropy analysis

作者：Zhao, Haitao[1,2];Wong, W. K.[1]

机构：[1]Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China;[2]E China Univ Sci & Technol, Automat Dept, Shanghai 200237, Peoples R China

年份：2014

卷号：47

期号：2

起止页码：806

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20134516958056);WOS:【SCI-EXPANDED(收录号:WOS:000329413000026)】；

基金：The authors would like to thank the anonymous reviewers and the financial support from the National Science Foundation of China (Project No. 61375012, 61375007, 61072090) and Shanghai Pujiang Program (Project No. 12PJ1402200).

语种：英文

外文关键词：Regularized discriminant entropy; Entropy-based learning; Discriminant entropy analysis

摘要：In this paper, we propose the regularized discriminant entropy (RDE) which considers both class information and scatter information on original data. Based on the results of maximizing the RDE, we develop a supervised feature extraction algorithm called regularized discriminant entropy analysis (RDEA). RDEA is quite simple and requires no approximation in theoretical derivation. The experiments with several publicly available data sets show the feasibility and effectiveness of the proposed algorithm with encouraging results. (C) 2013 Elsevier Ltd. All rights reserved.