融合语义差别和流型学习的偏标记学习方法.

Partial label learning is a weakly supervised learning framework. In partial label learning, each instance is associa-ted with a set of candidate labels, and its ground-truth label is unknown to us during the training process. In order to eliminate the ambiguous of candidate labels, this paper put forward a novel partial label learning by semantic difference and manifold learning (PL-SDML) method, which combined the semantic difference maximization criterion of instances and manifold lear-ning for partial label learning. The PL-SDML method was a two-stage method that used semantic difference maximization criterion of instances and manifold learning to generate the labeling confidence for training instances in the training phase. Then, PL-SDML made predicts for unseen instances via a nearest neighbor voting-based approach in the predict phase. On the UCI datasets, PL-SDML is superior to other comparison algorithms in 70% cases. On the four real-world datasets, the classification performance of PL-SDML improves by 0.3%~13.8% compared with other baselines. [ABSTRACT FROM AUTHOR]