计算机科学与探索2017，Vol.11Issue(7)：1175-1182,8.DOI:10.3778/j.issn.1673-9418.1605062
非负稀疏表示的多标签特征选择
Multi-Label Feature Selection via Non-Negative Sparse Representation
摘要
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 prob-lem, which is combined with non-negative constraint problem and L2,1- 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.关键词
多标签学习/特征选择/非负矩阵分解/L2,1-范数Key words
multi-label learning/feature selection/non-negative matrix factorization/L2/1-norm分类
信息技术与安全科学引用本文复制引用
蔡志铃,祝峰..非负稀疏表示的多标签特征选择[J].计算机科学与探索,2017,11(7):1175-1182,8.基金项目
The National Natural Science Foundation of China under Grant Nos. 61379049, 61379089 (国家自然科学基金面上项目). （国家自然科学基金面上项目）
