Multi-view multi-label learning for label-specific features via GLocal Shared Subspace Learning

In multi-label learning (MLL), label-specific feature (LSF) learning assumes that labels are determined by their inherent characteristics. However, in multi-view multi-label learning (MVMLL), the heterogeneity problem persists within the feature space. The views with varying dimensions can result in different dimensions of extracted LSF. Existing algorithms extract the LSF for each view separately, suffering the inadequate communication of the LSF and poor classification accuracy. The subspace learning method can address the dimension-inconsistency problem in multi-views by extracting extract the shared subspace for each view by substituting the original view feature space. However, the individual subspaces contain relatively homogeneous information. Based on this analysis, the GLocal Shared Subspace Learning (GLSSL) algorithm was proposed for multi-view multi-label learning to access more informative subspaces. First, the label groups were obtained through spectral clustering, entirely considering the correlation between the label groups and features to identify the specific relevant view features corresponding to each label group. Subsequently, the global shared subspace (global subspace) and local shared subspace (local subspace) were extracted from the original feature space and feature sets, respectively. Finally, the local subspace was complemented with the global subspace for LSF learning. The proposed algorithm was validated through comparative experiments with several state-of-the-art algorithms on multiple benchmark multi-view multi-label datasets.