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Multiview Clustering

  • Shiliang SunEmail author
  • Liang Mao
  • Ziang Dong
  • Lidan Wu
Chapter

Abstract

This chapter introduces three kinds of multiview clustering methods. We begin with the multiview spectral clustering, where the clustering is carried out through the partition of a relationship graph of the data. It depends on the eigenvector of the adjacent matrix of the data. Then we consider the multiview subspace clustering, which aims at recovering the underlying subspace of the multiview data and performs clustering on it. Finally, we introduce distributed multiview clustering, which first learns the patterns from each view individually and then combines them together to learn optimal patterns for clustering, and multiview clustering ensemble. It combines the results of multiple clustering algorithms to obtain better performance. We also briefly introduce some other methods at the end of this chapter.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

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