[1]严菲,王晓栋.鲁棒的半监督多标签特征选择方法[J].智能系统学报,2019,14(4):812-819.[doi:10.11992/tis.201809017]
YAN Fei,WANG Xiaodong.A robust, semi-supervised, and multi-label feature selection method[J].CAAI Transactions on Intelligent Systems,2019,14(4):812-819.[doi:10.11992/tis.201809017]
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YAN Fei,WANG Xiaodong.A robust, semi-supervised, and multi-label feature selection method[J].CAAI Transactions on Intelligent Systems,2019,14(4):812-819.[doi:10.11992/tis.201809017]
鲁棒的半监督多标签特征选择方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第4期
页码:
812-819
栏目:
学术论文—机器学习
出版日期:
2019-07-02
- Title:
- A robust, semi-supervised, and multi-label feature selection method
- Keywords:
- feature selection; semi-supervised learning; multi-label learning; l1-norm graph; linear regression; l2; 1-norm; robust; classification; clustering
- 分类号:
- TP391.4
- 摘要:
- 针对现有的半监督多标签特征选择方法利用l2-范数建立谱图易受到噪声影响的问题，文中提出一种鲁棒的半监督多标签特征选择方法，利用全局线性回归函数建立多标签特征选择模型，结合l1图获取局部描述信息提高模型准确度，引入l2，1约束提升特征之间可区分度和回归分析的稳定性，避免噪声干扰。在4种开源数据集上借助多种性能评价标准验证所提出方法，结果表明：本文方法能有效提高分类模型的准确性和对外界噪声的抗干扰性。
- Abstract:
- The existing semi-supervised multi-label feature selection method constructs a spectral image based on the l2-norm, which is sensitive to noise. To handle this problem, a robust semi-supervised multi-label feature selection method is presented in this study. A global linear regression function is utilized to construct the multi-label feature selection model, and the l1-norm graph is combined to obtain the local discriminant information. Subsequently, the l2,1-norm constraint is added to improve the distinguishability between these features and the stability of regression analysis to avoid noise interference. Four open source datasets are selected to verify the proposed method based on various evaluation criteria. The results demonstrate the efficiency of our method with respect to the classification accuracy and robustness.
- 参考文献/References:
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备注/Memo
收稿日期:2018-09-13。
基金项目:国家自然科学基金项目（61871464）；福建省自然科学基金面上项目（2017J01511）；福建省中青年教师科研项目（JAT170417）；厦门理工学院科研攀登计划项目（XPDKQ18012）.
作者简介:严菲. 女,1985年生,实验师,主要研究方向为特征选择、机器学习。主持福建省教育厅中青年教师项目1项。发表学术论文5篇;王晓栋,男,1983年生,副教授,博士,主要研究方向为机器学习、图像处理。主持福建省自然科学基金面上项目1项,福建教育厅中青年教师项目1项。发表学术论文10篇。
通讯作者:严菲.E-mail:fyan@xmut.edu.cn
更新日期/Last Update:
2019-08-25