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Multi-label feature selection with shared common mode
Pattern Recognition ( IF 8 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.patcog.2020.107344
Liang Hu , Yonghao Li , Wanfu Gao , Ping Zhang , Juncheng Hu

Abstract Multi-label feature selection plays an indispensable role in multi-label learning, which eliminates irrelevant and redundant features while retaining relevant features. Most of existing multi-label feature selection methods employ two strategies to construct feature selection models: extracting label correlations to guide feature selection process and maintaining the consistency between the feature matrix and the reduced low-dimensional feature matrix. However, the data information is described by two data matrices: the feature matrix and the label matrix. Previous methods devote attention to either of the two data matrices. To address this issue, we propose a novel feature selection method named Feature Selection considering Shared Common Mode between features and labels (SCMFS). First, we utilize Coupled Matrix Factorization (CMF) to extract the shared common mode between the feature matrix and the label matrix, considering the comprehensive data information in the two matrices. Additionally, Non-negative Matrix Factorization (NMF) is adopted to enhance the interpretability for feature selection. Extensive experiments are implemented on fifteen real-world benchmark data sets for multiple evaluation metrics, the experimental results demonstrate the classification superiority of the proposed method.

中文翻译:

具有共享共模的多标签特征选择

摘要 多标签特征选择在多标签学习中起着不可或缺的作用,它在保留相关特征的同时剔除不相关和冗余的特征。现有的多标签特征选择方法大多采用两种策略来构建特征选择模型:提取标签相关性以指导特征选择过程以及保持特征矩阵与降维后的低维特征矩阵之间的一致性。然而,数据信息由两个数据矩阵描述:特征矩阵和标签矩阵。以前的方法关注两个数据矩阵中的任何一个。为了解决这个问题,我们提出了一种新的特征选择方法,称为特征选择考虑特征和标签之间的共享共模(SCMFS)。第一的,我们利用耦合矩阵分解(CMF)来提取特征矩阵和标签矩阵之间的共享共模,考虑到两个矩阵中的综合数据信息。此外,采用非负矩阵分解(NMF)来增强特征选择的可解释性。在 15 个真实世界基准数据集上对多个评估指标进行了大量实验,实验结果证明了所提出方法的分类优越性。
更新日期:2020-08-01
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