5 research outputs found
Collaboration based Multi-Label Learning
It is well-known that exploiting label correlations is crucially important to
multi-label learning. Most of the existing approaches take label correlations
as prior knowledge, which may not correctly characterize the real relationships
among labels. Besides, label correlations are normally used to regularize the
hypothesis space, while the final predictions are not explicitly correlated. In
this paper, we suggest that for each individual label, the final prediction
involves the collaboration between its own prediction and the predictions of
other labels. Based on this assumption, we first propose a novel method to
learn the label correlations via sparse reconstruction in the label space.
Then, by seamlessly integrating the learned label correlations into model
training, we propose a novel multi-label learning approach that aims to
explicitly account for the correlated predictions of labels while training the
desired model simultaneously. Extensive experimental results show that our
approach outperforms the state-of-the-art counterparts.Comment: Accepted by AAAI-1