基于自编码器及超图学习的多标签特征提取  

Multi-label Feature Selection with Autoencoders and Hypergraph Learning

作　　者：唐朝辉[1,2] 朱清新[1] 洪朝群[2] 祝峰[3] 

机构地区：[1]电子科技大学信息与软件工程学院,成都611731 [2]厦门理工学院计算机与信息工程学院,厦门361024 [3]闽南师范大学粒计算实验室,漳州363000

出　　处：《自动化学报》2016年第7期1014-1021,共8页Acta Automatica Sinica

基　　金：国家自然科学基金(61300192;61472110;61573297;61379049);中央高校基本科研项目(ZYGX2014J052);福建省自然科学基金(2014J01256;2015J01277)资助~~

摘　　要：在实际应用场景中越来越多的数据具有多标签的特性,且特征维度较高,包含大量冗余信息.为提高多标签数据挖掘的效率,多标签特征提取已经成为当前研究的热点.本文采用去噪自编码器获取多标签数据特征空间的鲁棒表达,在此基础上结合超图学习理论,融合多个标签对样本间几何关系的影响以提升特征提取的性能,构建多标签数据样本间几何关系所对应超图的Laplacian矩阵,并通过Laplacian矩阵的特征值分解得到低维投影空间.实验结果证明了本文所提出的算法在分类性能上是有效可行的.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 multi-label 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.

关 键 词：深度学习 自编码器 多标签 超图 特征提取 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]