期刊
FRONTIERS OF COMPUTER SCIENCE IN CHINA
卷 5, 期 3, 页码 268-278出版社
HIGHER EDUCATION PRESS
DOI: 10.1007/s11704-011-0023-0
关键词
spectral clustering; random walk; probability transition matrix; matrix perturbation
类别
资金
- National Natural Science Foundation of China [60873180]
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.
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