Label generation with consistency on the graph for multi-label feature selection

Multi -label feature selection involves the selection of informative features in high -dimensional data sets based on the relationships among different variables. However, large-scale data sets often contain unknown labels that hold latent information, posing a challenge for discovery. Explicitly uncovering latent information among labels not only helps establish robust mappings between features and labels but also expands the applicable knowledge. This paper introduces a novel feature selection method called Label Generation with Consistency on the Graph for Multilabel Feature Selection (LGCM) to effectively integrate label generation and feature selection processes. The proposed method incorporates a two-way flipping mechanism that leverages the consistency on the global graph to guide label generation for each instance. Additionally, the feature selection model utilizes a shared low -rank feature space to minimize deviation from the label generation, providing feedback to the label generation process. Extensive experiments validate the effectiveness of LGCM in improving state-of-the-art feature selection methods in multi -label learning.