.png){kind=icon}
返回
Manifold regularized discriminative feature selection for multi-label learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2019.06.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In multi-label learning, objects are essentially related to multiple semantic meanings, and the type of data is confronted with the impact of high feature dimensionality simultaneously, such as the bioinformatics and text mining applications. To tackle the learning problem, the key technology, i.e., feature selection, is developed to reduce dimensionality, whereas most of the previous methods for multi-label feature selection are either directly transformed from traditional single-label feature selection methods or half-baked in the label information exploitation, and thus causing the redundant or irrelevant features involved in the selected feature subset. Aimed to seek discriminative features across multiple class labels, we propose an embedded multi-label feature selection method with manifold regularization. To be specific, a low-dimensional embedding is constructed based on the original feature space to fit the label distribution for capturing the label correlations locally, which is also constrained using the label information in consideration of the co-occurrence relationships of label pairs. Following this principle, we design an optimization objective function involving l(2,1)-norm regularization to achieve multi-label feature selection, and the convergence is guaranteed. Empirical studies on various multi-label data sets reveal that the proposed method can obtain highly competitive performance against some state-of-the-art multi-label feature selection methods. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Multi-label learning
Feature selection
Label correlations
Manifold regularization
Optimization objective
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
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
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
.png){kind=icon}
PREAI
分享收藏
Effects of stereoisomers of estradiol on food intake, body weight and hoarding behavior in female rats雌二醇立体异构体对雌性大鼠摄食、体重和囤积行为的影响
PREAI
分享收藏Towards a unified multi-source-based optimization framework for multi-label learning面向多标签学习的基于多源的统一优化框架
PREAI
分享收藏
查看更多内容