Noise Robust Spectral Clustering
Noise Robust Spectral Clustering
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DOI：
10.1109/iccv.2007.4409061
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发表时间：
2007-12
期刊：
影响因子：
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通讯作者：
Zhenguo Li;Jianzhuang Liu;Shifeng Chen;Xiaoou Tang
中科院分区：
文献类型：
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作者：
Zhenguo Li;Jianzhuang Liu;Shifeng Chen;Xiaoou Tang
本文旨在将抗噪鲁棒性引入谱聚类算法。首先，我们提出了一个翘曲模型，将数据映射到一个新的空间的正则化的基础上。在变形过程中，每个点将其空间信息平滑地传播到其他点。实验研究表明，经过变形后的聚类变得相对紧凑，并且分离良好，包括由噪声点形成的噪声聚类。在这个新的空间中，簇的数目可以通过特征值分析来估计。我们进一步将谱映射应用于数据以获得低维数据表示。最后，使用K-means算法进行聚类。该方法与以往的谱聚类方法相比，具有以下上级优点：（1）将噪声点归为一个新的类，对噪声具有较强的鲁棒性;（2）自动确定聚类数目和算法参数。对合成数据和真实的数据的实验结果证明了这种优越性。
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 and real data have demonstrated this superiority.