Weakly supervised label distribution learning based on transductive matrix completion with sample correlations

Abstract Label distribution learning (LDL) is one of the paradigms for dealing with label ambiguity, and it can learn the relative importance of each label to a particular instance. Most of the existing LDL approaches require strong supervision information, which always involves high cost of the data-labeling process. To this end, we propose a novel transductive weakly supervised LDL algorithm based on matrix completion, which simultaneously explores the discriminant knowledge of both training instances and testing instances. Moreover, the manifold regularization is exploited to capture the samples’ relevance to enhance the performance of matrix completion. Experimental results on real-world data sets with various missing percentages validate the effectiveness of our method.