  Multi-label learning deals with the problem that each instance is associated
with multiple labels simultaneously. Most of the existing approaches aim to
improve the performance of multi-label learning by exploiting label
correlations. Although the data augmentation technique is widely used in many
machine learning tasks, it is still unclear whether data augmentation is
helpful to multi-label learning. In this article, we propose to leverage the
data augmentation technique to improve the performance of multi-label learning.
Specifically, we first propose a novel data augmentation approach that performs
clustering on the real examples and treats the cluster centers as virtual
examples, and these virtual examples naturally embody the local label
correlations and label importances. Then, motivated by the cluster assumption
that examples in the same cluster should have the same label, we propose a
novel regularization term to bridge the gap between the real examples and
virtual examples, which can promote the local smoothness of the learning
function. Extensive experimental results on a number of real-world multi-label
datasets clearly demonstrate that our proposed approach outperforms the
state-of-the-art counterparts.
