Incremental Label Distribution Learning

Label distribution learning (LDL) has large practical application potentials due to its superiority in dealing with ambiguous label information. Most existing LDL methods are designed in a closed environment, wherein all the elements, e.g., feature and label space, are fixed. Nevertheless, in reality, data are dynamically acquired in the open environment, wherein the feature space can accumulate over time and the label space can be further enriched and refined accordingly with the accumulated feature space. Conducting LDL for such simultaneous augmentation of feature and label is crucial but rarely studied, particularly when the labeled samples with full observations are limited. In this paper, we propose a novel Incremental Label Distribution Learning (ILDL) method to tackle this brand new LDL problem by continuously transiting discriminative information from the previous model to the current one. Concretely, a prior compensation regularization is designed for such discriminative information transitivity. In this manner, the current model has the capacity to reuse the previous model to guide its own training. Furthermore, we present the theoretical analyses about the generalization bound, which provides guarantees for model inheritance. Comprehensive experimental studies validate the effectiveness of our proposal.