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基于地标选择与标签相关性的类属特征多标签学习

孙冬 谢欢欢 刘欣雨 赵大卫 高清维 卢一相 竺德

安徽大学学报(自然科学版)2026,Vol.50Issue(2):17-24,8.
安徽大学学报(自然科学版)2026,Vol.50Issue(2):17-24,8.DOI:10.3969/j.issn.1000-2162.2026.02.003

基于地标选择与标签相关性的类属特征多标签学习

Multi-label learning of label-specific features based on landmark selection and label correlation

孙冬 1谢欢欢 1刘欣雨 1赵大卫 1高清维 1卢一相 1竺德1

作者信息

  • 1. 安徽大学电气工程与自动化学院,安徽 合肥 230601
  • 折叠

摘要

Abstract

The real world contained a large number of objects that could be assigned multiple labels simultaneously.Being a crucial component of machine learning,multi-label learning algorithms had significant advantages in dealing with these complex semantic entities.Most of the existing multi-label learning algorithms directly predicted all labels.However,in practical applications,due to the redundancy of the label space,it was quite difficult to predict all the labels at the same time,so it was necessary to select a representative subset of labels as landmarks.Landmarks were correlated with other labels,which was crucial to improve the performance of the model.Based on this,this paper proposed a multi-label learning algorithm of label-specific features based on landmark selection and label correlation.Firstly,a linear regression model for multi-label learning was established and the label-specific features were obtained by using Li-norm.Then,the representative landmarks were selected by introducing a landmark selection matrix with soft constraints.Finally,the label correlation between landmarks and other labels was mined to further improve the performance of multi-label learning algorithms.Experiments demonstrated that the proposed algorithm competes favorably with other advanced multi-label learning algorithms.

关键词

多标签学习/类属特征/地标选择/标签相关性

Key words

multi-label learning/label-specific features/landmark selection/label correlation

分类

信息技术与安全科学

引用本文复制引用

孙冬,谢欢欢,刘欣雨,赵大卫,高清维,卢一相,竺德..基于地标选择与标签相关性的类属特征多标签学习[J].安徽大学学报(自然科学版),2026,50(2):17-24,8.

基金项目

国家自然科学基金资助项目(62071001) (62071001)

安徽省自然科学基金资助项目(2008085MF192,2008085MF183,2208085QF206,2308085QF224) (2008085MF192,2008085MF183,2208085QF206,2308085QF224)

安徽省教育厅高校自然科学重点项目(KJ2021A0013,KJ2021A0013) (KJ2021A0013,KJ2021A0013)

中国博士后面上基金资助项目(2023M730009) (2023M730009)

安徽大学学报(自然科学版)

1000-2162

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