Label distribution learning via second-order self-representation

Label distribution learning is an effective learning approach for addressing label polysemy in the field of machine learning. In contrast to multi-label learning, label distribution learning can accurately represent the relative importance of labels and has richer semantic information about labels. Presently label distribution learning algorithms frequently integrate label correlation into their models to narrow down the assumption space of the model. However, existing label distribution learning works on label correlation use one-to-one or many-to-one correlation which has limitations in representing more complex correlation relationships. To address this issue, we attempt to extend the existing correlation relationships to many-to-many relationships. Specifically, we first construct a many-to-many correlation mining framework based on self-representation. Then by using the learned many-to-many correlation, a label distribution learning algorithm is designed. Our algorithm achieved the best performance in \(78.21\%\) $78.21\%$ of cases across all datasets and all performance metrics with the algorithm having the best average ranking. It also demonstrated statistical superiority compared to the comparison algorithms in pairwise two-tailed t-tests. This paper introduces a novel approach to representing and applying label correlations in label distribution learning. The exploitation of this new many-to-many correlation can enhance the representational capabilities of label distribution learning models.