融入标签相关性先验的多视图多标签学习  

Multi-view Multi-label Learning with Label Correlation Priors

作　　者：盛思柔 欧阳宵 陶红 侯臣平[1] SHENG Sirou;OUYANG Xiao;TAO Hong;HOU Chenping(School of Science,National University of Defense Technology,Changsha 410073,China;School of Mathematics and Computational Science,Xiangtan University,Xiangtan,Hunan 411105,China)

机构地区：[1]国防科技大学理学院,长沙410073 [2]湘潭大学数学与计算科学学院,湖南湘潭411105

出　　处：《计算机科学》2025年第2期58-66,共9页Computer Science

基　　金：国家重点研发计划(2022ZD0114803);国家自然科学基金重点项目(62136005);国家自然科学基金(61922087,62006238);湖南省自然科学基金(2023JJ20052)。

摘　　要：多视图多标签学习作为一种广泛应用于图像分类、文本挖掘和生物信息学等多个领域的机器学习和数据挖掘技术,正受到研究者们的广泛关注。在此框架下,样本通常由多个视图进行表征,并且可以同时关联到多个标签。尽管已有大量方法被提出,但许多方法未能充分整合先验信息来提升学习性能,这往往导致预测性能不尽如人意。针对这一问题,文中提出了一种新的多视图多标签学习方法,称为融入标签相关性先验的多视图多标签学习(Multi-view multi-label Learning with Label Correlation Priors,MERIT)。该方法在无标签的训练数据的情况下,仅利用标签相关性先验作为弱监督信息来获取多标签预测模型,从而减少对大量标注数据的依赖。它不仅能自适应地调整不同视图的权重,还能最小化样本相似性与标签相似性之间的差异,从而更准确地描述同一组样本间的相似性。在8个多视图多标签数据集上的实验结果表明,与同类方法相比,MERIT展现出了更优越的性能。Multi-view multi-label learning,as a widely used machine learning and data mining technique in fields such as image classification,text mining,and bioinformatics,is receiving extensive attention from researchers.In this framework,samples are typically represented by multiple views and can be associated with multiple labels simultaneously.Although many methods have been proposed,many of them fail to fully integrate prior information to enhance learning performance,which often leads to unsa-tisfactory prediction performance.Aiming at this issue,this paper proposes a new multi-view multi-label learning method called multi-view multi-label learning with label correlation priors(MERIT).In the absence of labeled training data,this method acquires a multi-label prediction model by using only the prior of label correlations as weak supervision,thereby reducing the dependence on a large amount of annotated data.It not only adaptively adjusts the weights of different views but also accurately characterizes the similarity among samples of the same group by minimizing the discrepancy between sample similarity and label similarity.Experimental results on 8 multi-view multi-label datasets show that MERIT exhibits superior performance compared to similar methods.

关 键 词：多视图多标签 标签相关性 样本相似性 先验信息 自加权策略 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]