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

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.