Semi-supervised partial multi-label classification with low-rank and manifold constraints

Multi-Label Classification (MLC), processing data annotated with multiple labels simultaneously, is usually based on the assumption that the instances are all correctly labeled with the ground-truth labels. However this assumption is often not satisfied in real-world scenarios due to the high cost of manual labeling. In this paper, we consider a classification scenario in which only a part of the instances are redundantly annotated, and call this scenario a Semi-supervised Partial Multi-Label Classification (SPMLC). We propose a novel method Lion (semi-supervised partial multi-label classification with Low-rank and manIfOld coNstraints) to handle this particular scenario. Especially, the not exactly known ground-truth labels are regarded as latent labels, and the prediction is achieved by estimating the confidences of latent labels (i.e., the latent ground-truth confidences) in future data. Lion jointly learns the latent ground-truth confidences and the prediction model. The low-rank and manifold constraints are utilized to capture local label correlations and neighboring structure of instances, so as to accurately estimate the latent ground truth confidences. Extensive experimental results validate the effectiveness of Lion. (C) 2021 Elsevier B.V. All rights reserved.