Deep Incomplete Multi-View Network Semi-Supervised Multi-Label Learning with Unbiased Loss

Due to the explosive growth in data sources and label categories, multi-view multi-label learning has garnered widespread attention. However, multi-view multi-label data often exhibits incomplete features and a huge number of unlabeled instances, due to the technical limitations and high cost of manual labeling in practice. Learning for such simultaneous missing of view features and labels is crucial but rarely studied, particularly when the labeled samples are limited. In this paper, we tackle this problem by proposing a novel Deep Incomplete Multi-View Semi-Supervised Multi-Label Learning method (DIMvSML). Specifically, to improve high-level representations of missing features, deep graph network is firstly employed to recover the feature information with structural similarity relations. Meanwhile, we design the structure-specific deep feature extractors to obtain discriminative information and preserve the cross-view consistency for the recovered data with instance-level contrastive loss. Furthermore, to eliminate the bias of the estimate of the risk that the semi-supervised multi-label methods minimise, we design a safe estimate framework with an unbiased loss and improve its empirical performance by using pseudo-labels of unlabeled data. Besides, we provide both the theoretical proof of better estimate variance and the intuitive explanation of our debiased framework. Finally, extensive experimental results on public datasets validate the superiority of DIMvSML compared with state-of-the-art methods.