Noise-Resistant Commonality and Individuality Label Learning for Multiview Multilabel Feature Selection Using Fuzzy Mutual Information

With the improvement of data collection capabilities and the increase in data volume, multiview multilabel feature selection has gained widespread attention. In multiview multilabel feature selection, it is inevitable to consider both the consistency and complementarity information between views. However, many existing methods tend to independently extract consensus and complementary information, and this fragmented approach often leads to ambiguity in the information partitioning, which in turn generates noise. Furthermore, existing methods do not consider the correlation between views and labels when estimating the effectiveness of different views, which can affect the accuracy of view weights. To address these issues, we propose a multiview multilabel feature selection method that integrates a noise-resistant strategy with fuzzy mutual information for jointly learning commonality and individuality label structures. Specifically, first, we learn the commonality label matrix using nonnegative matrix factorization to explore the consistency between views. This matrix not only captures shared patterns across views but also helps the model better fit the ground-truth labels. Second, we learn individuality label matrices to capture the complementary information of each view, and introduce view noise label matrices to mitigate the impact of inherent noise in the true labels and noise labels generated by partitioning. Third, we incorporate view weights based on fuzzy mutual information, which can accurately reflect the correlation between views and labels to enhance important views while reducing the contribution of noise. Finally, we design a sparse model-based multiview multilabel feature selection method and provide theoretical proof of its convergence. Extensive experiments on multiple benchmark datasets are conducted to demonstrate the effectiveness of our approach.