一种新的多标记特征提取方法

doi:10.3969/j.issn.1001-2400.2016.06.011

西安电子科技大学学报

一种新的多标记特征提取方法

张居杰1;方敏1;郭锦2,3

(1. 西安电子科技大学计算机学院，陕西西安 710071;
2. 西北工业大学计算机学院，陕西西安 710072;
3. 西安工业大学电子信息工程学院，陕西西安 710021)

收稿日期:2015-09-07 出版日期:2016-12-20 发布日期:2017-01-19
通讯作者: 方敏(1965-)，女，教授
作者简介:张居杰(1987-)，男，西安电子科技大学博士研究生，E-mail：jujiezhang@stu.xidian.edu.cn．
基金资助:
国家自然科学基金资助项目(61472305, 61070143)；陕西省科学研究发展计划资助项目(2015GY027)；航空科学基金资助项目(20151981009)

New method for multi-label feature extraction

ZHANG Jujie1;FANG Min1;GUO Jin2,3

(1. School of Computer Science and Technology, Xidian Univ., Xi'an 710071, China;
2. School of Computer Science and Engineering, Northwestern Polytechnical Univ., Xi'an 710072, China;
3. College of Electronical and Information Engineering, Xi'an Technological Univ., Xi'an 710021, China)

Received:2015-09-07 Online:2016-12-20 Published:2017-01-19

摘要/Abstract

摘要：

针对已有多标记特征提取方法并未充分利用特征信息的问题，提出了基于希尔伯特-施密特独立标准和主成分分析的多标记特征提取方法．该方法通过使标记与降维后特征之间希尔伯特-施密特范数达到最大，以充分利用标记知识;同时利用主成分分析，以尽量减少特征提取过程中的协方差信息损失．通过在Yahoo数据集上的实验表明，该算法的性能优于主成分分析和当前3种主要的多标记特征提取方法，验证了该算法的有效性．

关键词: 多标记分类, 特征提取, 希尔伯特-施密特独立标准, 主成分分析

Abstract:

Existing multi-label feature extraction methods are limited by not fully exploiting feature information. To tackle this problem， this paper proposes a new method for multi-label feature extraction. First, it maximizes the Hilbert-Schmidt independence criterion (HSIC) between labels and the features after reducing dimensionality to exploit label information, while it minimizes the information loss using principal component analysis (PCA). Experiments across Yahoo demonstrate the effectiveness and superiority of the proposed method to PCA and 3 state-of-art multi-label feature extraction methods.

Key words: multi-label classification, feature extraction, Hilbert-Schmidt independence criterion, principal component analysis

张居杰;方敏;郭锦. 一种新的多标记特征提取方法[J]. 西安电子科技大学学报, 2016, 43(6): 62-67.

ZHANG Jujie;FANG Min;GUO Jin. New method for multi-label feature extraction[J]. Journal of Xidian University, 2016, 43(6): 62-67.

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一种新的多标记特征提取方法

New method for multi-label feature extraction

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