Label distribution learning by utilizing common and label-specific feature fusion space

Label Distribution Learning (LDL) is a novel machine learning paradigm that focuses on the description degrees of labels to a particular instance. Existing LDL algorithms generally learn with the original input space, that is, all features are simply employed in the discrimination processes of all class labels. However, this common-used data representation strategy ignores that each label is supposed to possess some specific characteristics of its own and therefore, may lead to sub-optimal performance. We propose label distribution learning by utilizing common and label-specific feature fusion space (LDL-CLSFS) in this paper. It first partitions all instances by label-value rankings. Second, it constructs label-specific features of each label by conducting clustering analysis on different instance categories. Third, it performs training and testing by querying the clustering results. Comprehensive experiments on several real-world label distribution data sets validate the superiority of our method against other LDL algorithms as well as the effectiveness of label-specific features.