In this paper, we propose a new nonlinear clustering method to preserve local structure of the features. Specifically, our method applies the gaussian kernel function to achieve high dimensional projection so as to make the original data linearly separable. Our method establishes the similarity matrix of data features in low-dimensional space to conduct local structure learning, as a result, it can avoid the divergence of sample sets and retain the original nearest neighbor structural relations. Furthermore, our method uses the sparse learning to remove the redundant features to make the model more robust in the process of learning. Experimental results on eight benchmark datasets show that our proposed method was superior to the state-of-the-art clustering methods in terms of clustering performance.

在本文中，我们提出了一种新的非线性聚类方法来保留特征的局部结构。具体而言，我们的方法应用高斯核函数来实现高维投影，从而使原始数据线性可分离。我们的方法建立了低维空间数据特征的相似度矩阵，进行局部结构学习，从而避免了样本集的差异，并保留了原始的最近邻结构关系。此外，我们的方法使用稀疏学习来消除冗余特征，从而使模型在学习过程中更加健壮。在八个基准数据集上的实验结果表明，我们提出的方法在聚类性能方面优于最新的聚类方法。