Abstract: 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.

Key words: Multi-view multi-label, Label correlation, Sample similarity, Prior information, Auto-weighted strategy