属性自表达的低秩无监督属性选择算法

doi:10.16088/j.issn.1001-6600.2018.01.008

广西师范大学学报（自然科学版） ›› 2018, Vol. 36 ›› Issue (1): 61-69.doi: 10.16088/j.issn.1001-6600.2018.01.008

属性自表达的低秩无监督属性选择算法

郑威,文国秋^*,何威,胡荣耀,赵树之

广西师范大学广西多源信息挖掘与安全重点实验室,广西桂林541004

收稿日期:2017-06-20 出版日期:2018-01-20 发布日期:2018-07-17
通讯作者: 文国秋(1987—),女,广西桂林人,广西师范大学讲师。E-mail:zwgxnu@163.com
基金资助:
国家自然科学基金(61573270);国家“973”计划项目(2013CB329404);中国博士后科学基金(2015M570837);广西自然科学基金(2015GXNSFCB139011) ;广西研究生教育创新计划项目(XYCSZ2017064,YCSW2017065)

Low-rank Unsupervised Feature Selection Based on Self-representation

ZHENG Wei,WEN Guoqiu^*,HE Wei,HU Rongyao,ZHAO Shuzhi

Guangxi Key Lab of Multi-source Information Mining and Security,Guangxi Normal University, Guilin Guangxi 541004,China

Received:2017-06-20 Online:2018-01-20 Published:2018-07-17

摘要/Abstract

摘要： 针对现有无监督属性约简方法只单一使用子空间学习或属性选择的方法,并且忽略数据之间的内在相关性,本文提出一种新的属性选择方法。首先提出一个属性自表达损失函数加上一个稀疏正则化(l_2,1-范数)实现无监督学习与属性选择。然后嵌入子空间学习方法,并使用低秩约束和图正则化项考虑数据的全局结构和局部结构。经聚类实验验证,该算法较对比算法能取得更好的效果。

关键词: 低秩约束, 属性选择, 子空间学习, 属性约简, 无监督

Abstract: A new method of feature selection is proposed to remedy several drawbacks with the existing unsupervised feature reduction method which only utilize the single subspace approach or feature selection method and ignores the inherent correlation within data. Specifically,a novel feature self-representation loss function is proposed to conduct unsupervised learning and feature selection by combining a sparse regularization (l_2,1-norm). And then,subspace learning method and low rank constraint with graph regularization are embeded into the model, to take into account of the global structure and local structure of the data,respectively. Experimental results show that the presented algorithm can achieve better results than some selected comparison algorithms.

Key words: low-rank constraint, feature selection, subspace learning, dimensionality reduction

中图分类号:

TP181

郑威,文国秋,何威,胡荣耀,赵树之. 属性自表达的低秩无监督属性选择算法[J]. 广西师范大学学报（自然科学版）, 2018, 36(1): 61-69.

ZHENG Wei,WEN Guoqiu,HE Wei,HU Rongyao,ZHAO Shuzhi. Low-rank Unsupervised Feature Selection Based on Self-representation[J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(1): 61-69.

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属性自表达的低秩无监督属性选择算法

Low-rank Unsupervised Feature Selection Based on Self-representation

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