Soft Computing

, Volume 21, Issue 8, pp 1937–1948 | Cite as

Multi-view spectral clustering via robust local subspace learning

Foundations

Abstract

Because of the existence of multiple sources of datasets, multi-view clustering has a wide range of applications in data mining and pattern recognition. Multi-view could utilize complementary information that existed in multiple views to improve the performance of clustering. Recently, there have been multi-view clustering methods which improved the performance of clustering to some extent. However, they do not take local representation relationship into consideration and local representation relationship is a crucial technology in subspace learning. To solve this problem, we proposed a novel multi-view clustering algorithm via robust local representation. We consider that all the views are derived from a robust unified subspace and noisy. To get the robust similarity matrix we simultaneously take all the local reconstruction relationships into consideration and use L1-norm to guarantee the sparsity. We give an iteration solution for this problem and give the proof of correctness. We compare our method with a number of classical methods on real-world and synthetic datasets to show the efficacy of the proposed algorithm.

Keywords

Multi-view learning Spectral clustering Robust local subspace learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Innovation and Entrepreneurship of DUTDalian University of TechnologyDalianChina
  2. 2.Student of Electronic Information and Electrical EngineeringDalian University of Technology DalianChina
  3. 3.Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

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