Local Structure Preservation for Nonlinear Clustering

摘要

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.