点击次数：

发布时间：2019-03-09

论文类型：期刊论文

发表时间：2011-09-01

发表刊物：FRONTIERS OF COMPUTER SCIENCE IN CHINA

收录刊物：CSCD、EI、SCIE、Scopus

卷号：5

期号：3

页面范围：268-278

ISSN号：1673-7350

关键字：spectral clustering; random walk; probability transition matrix; matrix perturbation

摘要：The construction process for a similarity matrix has an important impact on the performance of spectral clustering algorithms. In this paper, we propose a random walk based approach to process the Gaussian kernel similarity matrix. In this method, the pair-wise similarity between two data points is not only related to the two points, but also related to their neighbors. As a result, the new similarity matrix is closer to the ideal matrix which can provide the best clustering result. We give a theoretical analysis of the similarity matrix and apply this similarity matrix to spectral clustering. We also propose a method to handle noisy items which may cause deterioration of clustering performance. Experimental results on real-world data sets show that the proposed spectral clustering algorithm significantly outperforms existing algorithms.