k-Labelsets for Multimedia Classification with Global and Local Label Correlation

Multimedia data, e.g., text and images, can be associated with more than one label. Existing methods for multimedia data classification either consider label correlation globally by assuming that it is shared by all the instances; or consider label correlations locally by assuming that it is a pairwise label correlation and shared only in a local group of instances. In fact, both global and local correlations may occur in the real-world applications; and the label correlation cannot be confined to pairwise labels. In this paper, a novel and effective multi-label learning approach named GLkEL is proposed for multimedia data categorization. Briefly, a High-Order Label Correlation Assessment strategy named HOLCA is proposed by using approximated joint mutual information; and then GLkEL, which breaks the original label set into several of the most correlated and distinct combination of k labels (called k-labELsets) according to the HOLCA strategy, learns Global and Local label correlations simultaneously based on label correlation matrix. Comprehensive experiments across 8 data sets from different multimedia domains indicate that, it manifests competitive performance against other well-established multi-label learning methods.