Stream label distribution learning processing via broad learning system

Label distribution learning (LDL) is designed for label ambiguity problem which widely exists in tasks like image classification and sentiment analysis. Nevertheless, most existing LDL methods adopt a batch-wise processing strategy that is not applicable to stream data issues, like video semantic segmentation and object tracking in auto-driving applications. For these scenarios where dynamic new data is generated continuously, stream LDL processing can be applied to reflect the changes in time and improve the accuracy of online prediction. This paper proposes a novel broad learning system (BLS) based online label distribution learning framework (SLD_BLS). Broad learning system was adopted as the baseline model because its incremental weight updating scheme, excellent efficiency, and outstanding performance can process streaming data in dynamically changing environments. However, there are several challenges to adapt BLS for LDL: 1) BLS's update rule cannot process LDL data; 2) BLS cannot show the mapping relationship between feature space and label space; 3) BLS neglects the inter-label relationships. To tackle these challenges, 1) a new weight update rule for LDL is designed; 2) a manifold regularization is applied to exploit the feature space manifolds; 3) an explicit label collaborating approach is introduced to positively bootstrapping the model training. Extensive experiments on 13 label distribution datasets indicated the performance of SLD_BLS outperforms 9 state-of-the-art LDL models on most datasets with significant reductions in training time across 6 metrics.