LIP-MC: Multi-Constraint Label Independent Prediction in label distribution learning

Abstract

Label distribution learning can resolve label ambiguity precisely by determining how well each label describes an instance. Traditional label distribution learning algorithms frequently attempt to model the complex relationships between all labels. This not only adds complexity to the model but may also reduce prediction accuracy due to label conflicts. In this paper, we propose a novel label distribution learning algorithm based on Multi-Constraint Label Independent Prediction (LIP-MC), intended to promote the rationality and accuracy of prediction results by simplifying the prediction process and combining multiple constraints. Specifically, label independent prediction values are generated for each label through sparsity constraints and weight coefficient matrices. Subsequently, a novel transformation model is designed to combine all separate label predictions and produce the final label distribution. Furthermore, smoothness constraints and logarithmic similarity constraints were introduced to enhance the model’s performance and generalization ability. On fourteen real datasets, the experiment was carried out, and the comparison results against seven advanced algorithms under seven evaluation metrics confirmed that the proposed algorithm is superior.