Privileged label enhancement with multi-label learning

Wenfang Zhu, Xiuyi Jia, Weiwei Li
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
Label distribution learning has attracted more and more attention in view of its more generalized ability to express the label ambiguity. However, it is much more expensive to obtain the label distribution information of the data rather than the logical labels. Thus, label enhancement is proposed to recover the label distributions from the logical labels. In this paper, we propose a novel label enhancement method by using privileged information. We first apply a multi-label learning model to
more » ... licitly capture the complex structural information between instances and generate the privileged information. Second, we adopt LUPI (learning with privileged information) paradigm to utilize the privileged information and employ RSVM+ as the prediction model. Finally, comparison experiments on 12 datasets demonstrate that our proposal can better fit the ground-truth label distributions.
doi:10.24963/ijcai.2020/325 dblp:conf/ijcai/WeiHHML20 fatcat:mbkbqui6vndivibwldz5acwes4