基于相关熵和流形学习的多标签特征选择算法

Multi-label feature selection algorithm based on correntropy and manifold learning

从相关熵的角度出发,提出一种基于相关熵和特征流形学习的稀疏正则化方法,用于解决多标签特征选择问题。在相关熵定义的基础上给出多标签特征选择的回归模型;结合l_(2,1)范数的性质和特征流形学习的定义建立基于相关熵和特征流形学习的稀疏正则化多标签特征选择模型及算法;证明该算法的收敛性并且通过试验验证所给算法的有效性。

A sparse regularization method based on correntropy and feature manifold learning was proposed to solve the problem of multi-label feature selection.A regression model of multi-label feature selection was presented by means of correntropy.The sparse regularized multi-label feature selection model,combing l2,1 norm and feature manifold learning,was established.An iterative algorithm was proposed for the above model.The convergence of the algorithm was proved and the effectiveness of the given algorithm was verified through experiments.