DLSA: Semi-supervised partial label learning via dependence-maximized label set assignment

Semi-supervised partial label learning is an emerging weakly supervised learning para-digm dealing with partially labeled data and unlabeled data simultaneously. The supervi-sion information is weak and even missing for training instances. Most existing researches simply propagate labels to unlabeled data and focus on inducing a weighted classifier model, ignoring the importance of acquiring reliable label confidences for training instances. In this paper, by label set assignment, the valid label supervision information can be preserved and propagated to unlabeled instances to a greater extent. Then, the training instances are projected into a lower-dimensional feature space. Reliable label con-fidence can be obtained by maximizing the dependence between the feature description and the associated label information. After that, the predictive model can be induced by preserving the intrinsic manifold structure in both feature space and label space. Accordingly, a novel semi-supervised partial label learning approach via dependence -maximized label set assignment (DLSA) is proposed, where an accurate and robust model can be induced based on reliable label confidences obtained by label set assignment and dependence-maximized dimensionality reduction. Extensive experiments on five real -world data sets validate the effectiveness and superiority of the proposed method.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

Semi-supervised partial label learning is a learning method that deals with partially labeled and unlabeled data. This paper proposes a novel approach by label set assignment and dependence-maximized dimensionality reduction to obtain reliable label confidences. Extensive experiments validate the effectiveness and superiority of the proposed method.