增强学习标签相关性的多标签特征选择方法  

Multi-label feature selection method with enhanced learning of label correlations

作　　者：滕少华[1] 卢建磊 滕璐瑶 张巍[1] Teng Shaohua;Lu Jianlei;Teng Luyao;Zhang Wei(School of Computer,Guangdong University of Technology,Guangzhou 510006,China;School of Information Engineering,Guangzhou Panyu Polytechnic,Guangzhou 511483,China)

机构地区：[1]广东工业大学计算机学院,广州510006 [2]广州番禺职业技术学院信息工程学院,广州511483

出　　处：《计算机应用研究》2024年第7期2079-2086,共8页Application Research of Computers

基　　金：国家自然科学基金资助项目(6197210);广州市科技计划资助项目(2023A04J1729)。

摘　　要：针对现有多标签特征选择方法存在的两个问题:第一,忽略了学习标签相关性过程中噪声信息的影响;第二,忽略探索每个簇的综合标签信息,提出一种增强学习标签相关性的多标签特征选择方法。首先,对样本进行聚类,并将每个簇中心视为一个综合样本语义信息的代表性实例,同时计算其对应的标签向量,而这些标签向量体现了每个簇包含不同标签的重要程度;其次,通过原始样本和每个簇中心的标签级自表示,既捕获了原始标签空间中的标签相关性,又探索了每一个簇内的标签相关性;最后,对自表示系数矩阵进行稀疏处理,以减少噪声的影响,并将原始样本和每个簇代表性实例分别从特征空间映射到重构标签空间进行特征选择。在9个多标签数据集上的实验结果表明,所提算法与其他方法相比具有更好的性能。Aiming at two problems of existing multi-label feature selection methods:first,ignoring the influence of noise information in the process of learning label correlations;second,neglecting to explore the comprehensive label information of each cluster,the paper proposed a multi-label feature selection method that enhanced label correlation learning.Initially,it clustered the samples and treated each cluster center as a representative instance of the comprehensive semantic information of the samples,while computing its corresponding label vectors which reflected the importance of different labels contained in each cluster.Then,through the label-level self-representation of the original samples and the center of each cluster,it both captured the label correlations in the original label space,and explored the label correlations within each cluster.Finally,the self-representation coefficient matrix was sparse to reduce the effect of noise,and the original sample and the representative instance of each cluster were mapped from the feature space to the reconstructed label space for feature selection.Experimental results on nine multi-labeled datasets show that the proposed algorithm has better performance compared with other methods.

关 键 词：多标签学习 特征选择 标签相关性 聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]