Clustering with adaptive manifold structure learning

Zhao Kang, Chong Peng, Qiang Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations


Construction of a reliable similarity matrix is fundamental for graph-based clustering methods. However, most of the current work is built upon some simple manifold structure, whereas limited work has been conducted on nonlinear data sets where data reside in a union of manifolds rather than a union of subspaces. Therefore, we construct a similarity graph to capture both global and local manifold structures of the input data set. The global structure is exploited based on the self-expressive property of data in an implicit feature space using kernel methods. Since the similarity graph computation is independent of the subsequent clustering, the final results may be far from optimal. To overcome this limitation, we simultaneously learn similarity graph and clustering structure in a principled way. Experimental studies demonstrate that our proposed algorithms deliver consistently superior results to other state-of-The-Art algorithms.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
Number of pages4
ISBN (Electronic)9781509065431
StatePublished - May 16 2017
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: Apr 19 2017Apr 22 2017

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference33rd IEEE International Conference on Data Engineering, ICDE 2017
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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