Spectral embedded clustering

Authors:
Feiping Nie;Dong Xu;Ivor W. Tsang;Changshui Zhang
Affiliations:
State Key Laboratory on Intelligent Techn. and Systems, Tsinghua National Laboratory for Information Sci. and Tech., Dept. of Automation, Tsinghua Univ., Beijing, China and School of Com. Eng., Na ...;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;State Key Laboratory on Intelligent Tech. and Systems, Tsinghua National Laboratory for Information Science and Technology, Dept. of Automation, Tsinghua Univ., Beijing, China
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

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Abstract

In this paper, we propose a new spectral clustering method, referred to as Spectral Embedded Clustering (SEC), to minimize the normalized cut criterion in spectral clustering as well as control the mismatch between the cluster assignment matrix and the low dimensional embedded representation of the data. SEC is based on the observation that the cluster assignment matrix of high dimensional data can be represented by a low dimensional linear mapping of data. We also discover the connection between SEC and other clustering methods, such as spectral clustering, Clustering with local and global regularization, K-means and Discriminative K-means. The experiments on many real-world data sets show that SEC significantly out-performs the existing spectral clustering methods as well as K-means clustering related methods.