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Privileged label enhancement with multi-label learning
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
doi:10.24963/ijcai.2020/325
dblp:conf/ijcai/WeiHHML20
fatcat:mbkbqui6vndivibwldz5acwes4