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Multi-view Clustering with Complete Information

  • Zhengming Ding
  • Handong Zhao
  • Yun Fu
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

Multi-view Clustering (MVC) has garnered more attention recently since many real-world data are comprised of different representations or views. The key is to explore complementary information to benefit the clustering problem. In this chapter, we consider the conventional complete-view scenario. Specifically, in the first section, we present a deep matrix factorization framework for MVC, where semi-nonnegative matrix factorization is adopted to learn the hierarchical semantics of multi-view data in a layer-wise fashion. In the second section, we make an extension and consider the different sampled feature sets as multi-view data. We propose a novel graph-based method, Ensemble Subspace Segmentation under Block-wise constraints (ESSB), which is jointly formulated in the ensemble learning framework.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indiana University-Purdue University IndianapolisIndianapolisUSA
  2. 2.Adobe ResearchSan JoseUSA
  3. 3.Northeastern UniversityBostonUSA

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