Active Label Distribution Learning

Abstract Label Distribution Learning (LDL) is a new learning paradigm to describe supervision as probability distribution and has been successfully applied in many real-world scenarios in recent years. In LDL applications, the availability of a large amount of labeled data guarantees the prediction performance. In this paper, we cogitate the active learning for LDL to reduce the annotation cost. The center element in practice any active learning strategy is building the criterion that measures the usefulness of the unlabeled data and decides the instances to be selected to label manually. We are probably the first to focus on active instance selecting for label distribution learning. We propose a strategy named Active Label Distribution Learning (ALDL) to select the most informative instances for LDL applications. The fundamental idea of the ALDL strategy is to quantify the degree of disagreement for each unlabeled instance by the committee consisted of selected LDL algorithms, and identify the instances to be labeled manually. ALDL maintains composing the committee with selected LDL algorithms and measure the value of unlabeled instances, and a weight vector is used both parts. Besides, we discuss the convergence and the parameter selecting of ALDL. Finally, compared with other active learning methods, the experimental results on the datasets show the effectiveness of our method.