  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.
