Many embedded feature selection methods ignore the correlation among the important features. To reduce correlation, some models introduce constraints to impose sparsity on features, some try to exploit the similarity and group features without changing the objective function. In this paper, we propose diverse feature selection (DFS), which simultaneously performs feature clustering and selection. Given a dataset with known class labels, we separate the features into a set of feature clusters where the features in the same cluster have a higher correlation with each other than with the features in different clusters. A diverse regularization (DR) is proposed to reduce the linear and nonlinear correlations among important features. Using this regularization, DFS can select features that are both informative and diverse. The experimental results on seven image datasets, five gene datasets as well as four other datasets demonstrate the superior performance of DFS.

许多嵌入式特征选择方法忽略了重要特征之间的相关性。为了减少相关性，一些模型引入了约束来对特征施加稀疏性，一些模型试图在不改变目标函数的情况下利用相似性和分组特征。在本文中，我们提出了多样化特征选择（DFS），它同时执行特征聚类和选择。给定具有已知类标签的数据集，我们将特征分成一组特征簇，其中同一簇中的特征彼此之间的相关性高于不同簇中的特征。一个多元化的正规化(DR) 被提出来减少重要特征之间的线性和非线性相关性。使用这种正则化，DFS 可以选择信息丰富且多样化的特征。在七个图像数据集、五个基因数据集以及其他四个数据集上的实验结果证明了 DFS 的优越性能。