基于不相关回归和自适应谱图的多标签学习特征选择方法  

MULTI-LABEL LEARNING AND FEATURE SELECTION METHOD BASED ON UNCORRELATED REGRESSION AND ADAPTIVE SPECTRUM

作　　者：王艳红 顾建伟 栾卫平 张睿 黄征 王达琳 Wang Yanhong;Gu Jianwei;Luan Weiping;Zhang Rui;Huang Zheng;Wang Dalin(State Grid Weihai Power Supply Company,Weihai 264200,Shandong,China;State Grid Info-Telecom Greate Power Science and Technology Co.,Ltd.,Fuzhou 350003,Fujian,China)

机构地区：[1]国网威海供电公司,山东威海264200 [2]国网信通亿力科技有限责任公司,福建福州350003

出　　处：《计算机应用与软件》2025年第3期298-310,391,共14页Computer Applications and Software

基　　金：国网山东省电力公司科技项目(5206041801H7)。

摘　　要：为解决特征的冗余性问题,提出一种基于不相关回归和自适应谱图的多标签学习特征选择方法。利用具有不相关约束的回归模型来生成低冗余但有区别的特征子集,从而同时进行流形学习和特征选择;在流形框架中引入基于信息熵的谱图项,以保持后续学习过程中数据的局部几何结构;在多个公共多标签数据集上进行综合实验,结果表明该方法能够高效和准确地实现高维数据特征选择。To solve the problem of feature redundancy,a multi-label learning and feature selection method based on uncorrelated regression and adaptive spectrum is proposed.The regression model with uncorrelated constraints was used to generate low-redundant but differentiated feature subsets,so that manifold learning and feature selection could be carried out simultaneously.The spectral term based on information entropy was introduced into the manifold framework to ensure the local geometric structure of the data in the subsequent learning process.Comprehensive experiments on multiple common multiple label data sets show that the proposed method can achieve high-dimensional data feature selection efficiently and accurately.

关 键 词：特征选择 高维数据 流形框架 信息熵 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]