Journal of Frontiers of Computer Science and Technology ›› 2017, Vol. 11 ›› Issue (7): 1175-1182.DOI: 10.3778/j.issn.1673-9418.1605062

Previous Articles     Next Articles

Multi-Label Feature Selection via Non-Negative Sparse Representation

CAI Zhiling, ZHU William+   

  1. Lab of Granular Computing, Minnan Normal University, Zhangzhou, Fujian 363000, China
  • Online:2017-07-01 Published:2017-07-07

非负稀疏表示的多标签特征选择

蔡志铃,祝  峰+   

  1. 闽南师范大学 粒计算重点实验室,福建 漳州 363000

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  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.

Key words: multi-label learning, feature selection, non-negative matrix factorization;L2,1-norm

摘要: 在多标签学习中,数据降维是一项重要而又具有挑战性的任务。特征选择是一种高效的数据降维技术,它通过保持最大相关信息选取一个特征子集。通过对子空间学习的研究,提出了基于非负稀疏表示的多标签特征选择方法。该方法可以看成是矩阵分解问题,其融合了非负约束问题和L2,1-范数最小优化问题。设计了一种高效的矩阵更新迭代算法求解新问题,并证明其收敛性。最后,对6个实际的数据集进行了测试,实验结果证明了算法的有效性。

关键词: 多标签学习, 特征选择, 非负矩阵分解, L2, 1-范数