Semi-Supervised Label Distribution Learning with Co-regularization

Label Distribution Learning (LDL) is a machine learning paradigm which is recently proposed to deal with the more ambiguity label. This paradigm assigns the distribution-level label to an instance so that it can exploit the relative importance of every candidate labels to a particular instance. Previous studies always concentrate on the methods under strong supervision, which requires a large number of tagged training data. In real-world applications, it is usually difficult to collect numerical precise labels owing to the large costs in labor and time spent on the label annotation. To this end, this paper proposes a novel algorithm named Semi-Supervised Label Distribution Learning with Co-regularization (S2LDL-CO). To benefit from all available information, ensemble of two different models is utilized to deal with the labeled and unlabeled data, respectively. More specifically, the co-regularization framework is adopted to combine these two different models, which can process both the labeled and unlabeled data with good robustness and consistency. What’s more, manifold regularization and l2,1-norm are also added into the objective function, which can fully exploit the implicit information in instances. Finally, the well-designed objective function is optimized by an Alternating Direction of Method of Multipliers (ADMM) algorithm. Experimental results tested on thirteen benchmark datasets illustrate its effectiveness over several state-of-the-art methods.