Multi-scale locality preserving projection for partial multi-view incomplete multi-label learning.

Amidst advancements in feature extraction techniques, research on multi-view multi-label classifications has attracted widespread interest in recent years. However, real-world scenarios often pose a challenge where the completeness of multiple views and labels cannot be ensured. At present, only a handful of techniques have attempted to address the complex issue of partial multi-view incomplete multi-label classification, and the majority of these approaches overlook the significance of manifold structures between instances. To tackle these challenges, we propose a novel partial multi-view incomplete multi-label learning model, termed MSLPP. Differing from existing studies, MSLPP emphasizes retaining the effective inherent structure of data during the feature extraction process, thereby facilitating a richer semantic information extraction. Specifically, MSLPP captures and integrates four types of information: the distance and similarity information in the original feature space, and the distance and similarity information in the extracted feature space. Further, by adopting the graph embedding technique, it simultaneously preserves the intrinsic structure with multi-scale information through a constraint term. Moreover, taking into account the negative impact of the missing views on the model and the possible impact of missing views on the data inherent structure, we further propose a shielding strategy for missing views, which not only eliminates the negative effects of missing views on the model but also more accurately captures the inherent data structure. The experimental results on five widely recognized datasets indicate that the model performs better than many excellent methods.