Active label distribution learning

Label Distribution Learning (LDL) is a new learning paradigm to describe supervision as probability dis-tribution and has been successfully applied in many real-world scenarios in recent years. In LDL applica-tions, 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 prac-tice 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 funda-mental 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 unla-beled 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. ? 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper discusses active learning methods in label distribution learning and proposes a strategy named Active Label Distribution Learning (ALDL) to select the most informative instances by quantifying the disagreement of unlabeled instances. The ALDL strategy maintains composing a committee with selected LDL algorithms to measure the value of unlabeled instances, and it uses a weight vector for both parts.