Random Manifold Sampling and Joint Sparse Regularization for Multi-label Feature Selection
This is an incremental improvement for multi-label learning tasks, enhancing feature selection by addressing specific bottlenecks like multicollinearity and robustness.
The paper tackles the problem of multi-label feature selection by proposing a model that combines $\ell_{2,1}$ and $\ell_{F}$ regularization to address multicollinearity and uses random walk-based manifold regularization for robustness, with experiments showing it outperforms other methods on real-world datasets.
Multi-label learning is usually used to mine the correlation between features and labels, and feature selection can retain as much information as possible through a small number of features. $\ell_{2,1}$ regularization method can get sparse coefficient matrix, but it can not solve multicollinearity problem effectively. The model proposed in this paper can obtain the most relevant few features by solving the joint constrained optimization problems of $\ell_{2,1}$ and $\ell_{F}$ regularization.In manifold regularization, we implement random walk strategy based on joint information matrix, and get a highly robust neighborhood graph.In addition, we given the algorithm for solving the model and proved its convergence.Comparative experiments on real-world data sets show that the proposed method outperforms other methods.