Implicit Regularization for Multi-label Feature Selection
Researchers have developed a novel method for feature selection in multi-label learning, utilizing implicit regularization and label embedding. This approach employs a Hadamard product parameterization, diverging from traditional methods that rely on explicit regularization terms. The proposed estimator aims to reduce bias and potentially mitigate overfitting by incorporating a latent semantic understanding of multi-label information. AI