Multi-label feature selection with autoencoders and hypergraph learning

被引：0

作者：

Tang C.-H. ^{[1
,2
]}

Zhu Q.-X. ^{[1
]}

Hong C.-Q. ^{[2
]}

Zhu W. ^{[3
]}

机构：

[1] School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu

[2] School of Computer and Information Engineering, Xiamen University of Technology, Xiamen

[3] Laboratory of Granular Computing, Minnan Normal University, Zhangzhou

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2016年 / 42卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Autoencoders; Deep learning; Feature selection; Hypergraph; Multi-label;

D O I：

10.16383/j.aas.2016.c150736

中图分类号：

学科分类号：

摘要：

In practical application scenarios, more and more data tend to be assigned with multiple labels and contain much redundant information in the high dimensional feature space. To improve the efficiency and effectiveness of multilabel data mining, multi-label data feature selection has become a hotspot. This paper utilizes denoising autoencoders to obtain a more robust version of multi-label data feature representation. Furthermore, based on hypergraph learning theory, a hypergraph Laplacian matrix corresponding to multi-label data is constructed by fusing the effects of all labels on geometrical relationship among all the samples, and then a projection space with lower dimension is obtained by conducting eigenvalue decomposition of the Laplacian matrix. Experimental results demonstrate the effectiveness and feasibility of the proposed algorithm according to its multi-label data classification performance. Copyright © 2016 Acta Automatica Sinica. All rights reserved.

引用

页码：1014 / 1021

页数：7

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