RG4LDL: Renormalization group for label distribution learning

Label distribution learning (LDL) is an effective paradigm to address label ambiguity by modeling the relevance of multiple labels to an instance. However, existing LDL methods suffer from challenges such as high model complexity, slow convergence, and limited availability of label distribution-annotated training data. To tackle these issues, we propose RG4LDL, a novel framework that integrates the renormalization group (RG) principle with LDL for the first time. RG4LDL employs a restricted Boltzmann machine (RBM)-based neural network to iteratively extract relevant degrees of freedom, thereby optimizing feature learning and improving predictive accuracy. By combining unsupervised RG learning and supervised LDL prediction in an end-to-end manner, RG4LDL achieves both efficiency and effectiveness. Experimental results on 13 real-world datasets and a synthetic toy dataset demonstrate that RG4LDL significantly outperforms state-of-the-art LDL methods in terms of predictive accuracy and computational efficiency. These results highlight the potential of RG4LDL as a benchmark solution for label distribution learning tasks.