  The problem of estimating the probability distribution of labels has been
widely studied as a label distribution learning (LDL) problem, whose
applications include age estimation, emotion analysis, and semantic
segmentation. We propose a tree-Wasserstein distance regularized LDL algorithm,
focusing on hierarchical text classification tasks. We propose predicting the
entire label hierarchy using neural networks, where the similarity between
predicted and true labels is measured using the tree-Wasserstein distance.
Through experiments using synthetic and real-world datasets, we demonstrate
that the proposed method successfully considers the structure of labels during
training, and it compares favorably with the Sinkhorn algorithm in terms of
computation time and memory usage.
