Sparse low-redundancy multilabel feature selection based on dynamic local structure preservation and triple graphs exploration

Much semantic information is involved in multilabel data due to more than one label associated with each instance. The redundant features and noise challenge knowledge mining in multilabel data. Constructing a learning model with discriminative features is essential for multilabel learning. Sparse graph-based methods simultaneously consider the topological structure, complex relations between features and labels, and the significance of features. However, three challenges exist. First, they either consider local label correlation or local label relevance and are complementary in the feature selection process. Second, existing methods use low-quality static graphs to explore local label correlations that result in degraded performance. Finally, only some methods deal with redundant features. A ridge regression-based sparse multilabel learning is proposed in this study to address these problems. The global and local label correlation are explored by preserving the instance-level graph structure to obtain a robust low-dimensional pseudo-label matrix to construct a high-quality dynamic label-level graph. Meanwhile, it preserves the feature-level graph structure to select low-redundant features. In addition, a new ������2,1/2-2-norm is designed to maintain the high-row sparsity of the model. The above items are embedded into a unified multilabel learning framework. A simple and effective optimization solution is finally designed and compared with eight relevant algorithms on twelve public benchmark data sets. The results demonstrate that the algorithm can improve classification performance.