.png){kind=icon}
返回
Robust multi-label feature selection with dual-graph regularization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2020.106126.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-label learning is facing great challenges due to high-dimensional feature space, complex label correlations and noises in multi-label data. Feature selection techniques have attracted considerable attention to address the problems. In this paper, we design our method based on dual-graph regularization, i.e., feature graph regularization and label graph regularization. The feature graph regularization is used to preserve the geometric structure of features, while label graph regularization intends to explore the correlations of labels. Furthermore, the l(2,1)-norm is imposed on the loss function to enhance the robust of feature selection methods. As a result, a new feature selection method termed Robust Multi-label Feature Selection based on Dual-graph (DRMFS) is proposed. Particularly, only one unknown variable, feature weight matrix, is incorporated in our proposed method, which can reach global optimum. Additionally, we impose both l(2,1)-norm and non-negative constraints onto the feature weight matrix to enhance the property of row-sparse. Finally, we design an optimization scheme to solve the proposed method, and offer the convergence proof of the optimization scheme. Extensive experimental results demonstrate the superiority of the proposed method in comparison to the-state-of-art multi-label feature selection methods. Finally, some insightful discussions with respect to the convergence analysis, complexity analysis and parameter sensitivity analysis are presented. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Feature selection
Multi-label learning
Graph regularization
Classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
机构
引用论文
分享收藏Multi-view manifold regularized learning-based method for prioritizing candidate disease miRNAs
PREAI
分享收藏Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification用于多标签分类的学习标签特定特征和类别相关标签
PREAI
分享收藏Unsupervised feature selection via latent representation learning and manifold regularization.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏
查看更多内容