非负稀疏表示的多标签特征选择

Multi-Label Feature Selection via Non-Negative Sparse Representation

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L_(2,1)-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。

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.