Proceedings

Spectral Embedded Clustering

Feiping Nie, Dong Xu, Ivor W. Tsang, Changshui Zhang

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Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1181-1185.

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 outperforms the existing spectral clustering methods as well as K-means clustering related methods.Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changshui Zhang

Feiping Nie, Dong Xu, Ivor W. Tsang, and Changshui Zhang. 2009. Spectral Embedded Clustering. In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09. IJCAI, 1181–1185. https://ijcai.org/proceedings/2009/199
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