基于流形学习的约束Laplacian分值多标签特征选择

多标签特征选择是针对多标签数据的特征选择技术，提高多标签分类器性能的重要手段。提出一种基于流形学习的约束Laplacian分值多标签特征选择方法（Manifold-based Constraint Laplacian Score，M-CLS）。方法分别在数据特征空间和类别标签空间定义两种Laplacian分值：在特征空间利用逻辑型类别标签的相似性对邻接矩阵进行改进，定义特征空间的约束Laplacian分值；在标签空间基于流形学习将逻辑型类别标签映射为数值型，定义实值标签空间的Laplacian分值。将两种分值的乘积作为最终的特征评价指标。实验结果表明，所提方法性能优于多种多标签特征选择方法。

Manifold learning based constraint Laplacian score multi-label feature selection

Multi-label feature selection is a feature selection technique based on multi-label data, which is an important means to improve the performance of multi-label classifiers. This paper proposes a Manifold-based Constraint Laplacian Score（M-CLS） feature selection method. The proposed method defines two kinds of Laplacian scores in two different spaces, namely the data feature space and label space. In the feature space, the similarity of the logical labels is used to modify the adjacency matrix, and a constraint laplacian score is defined. In the label space, the logical labels are extended to the numeric labels through manifold learning, and a Laplacian score is defined based on the real values of labels. The product of the two scores is the final feature evaluation index. Experiments show that the proposed method outperforms several multi-label feature selection methods.