Noise Robust Spectral Clustering

10 years 5 months ago
Noise Robust Spectral Clustering
This paper aims to introduce the robustness against noise into the spectral clustering algorithm. First, we propose a warping model to map the data into a new space on the basis of regularization. During the warping, each point spreads smoothly its spatial information to other points. After the warping, empirical studies show that the clusters become relatively compact and well separated, including the noise cluster that is formed by the noise points. In this new space, the number of clusters can be estimated by eigenvalue analysis. We further apply the spectral mapping to the data to obtain a low-dimensional data representation. Finally, the K-means algorithm is used to perform clustering. The proposed method is superior to previous spectral clustering methods in that (i) it is robust against noise because the noise points are grouped into one new cluster; (ii) the number of clusters and the parameters of the algorithm are determined automatically. Experimental results on synthetic a...
Zhenguo Li, Jianzhuang Liu, Shifeng Chen, Xiaoou T
Added 14 Oct 2009
Updated 30 Oct 2009
Type Conference
Year 2007
Where ICCV
Authors Zhenguo Li, Jianzhuang Liu, Shifeng Chen, Xiaoou Tang
Comments (0)