关键词: 多标签学习, 特征选择, 非负矩阵分解, L_2, 1-范数

Abstract: Dimensionality reduction is an important and challenging task in multi-label learning. Feature selection is a highly efficient technique for dimensionality reduction by maintaining maximum relevant information to find an optimal feature subset. First of all, this paper proposes a multi-label feature selection method based on non-negative sparse representation by studying the subspace learning. This method can be treated as a matrix factorization problem, which is combined with non-negative constraint problem and  L_2,1 norm minimization problem. Then, this paper designs a kind of efficient iterative update algorithm to tackle the new problem and proves its convergence. Finally, the experimental results on six real-world data sets show the effectiveness of the proposed algorithm.

Key words: multi-label learning, feature selection, non-negative matrix factorization;L_2,1-norm