Convex Constrained Clustering with Graph-Laplacian Pca

Yuheng JIA, Sam KWONG, Junhui HOU, Wenhui WU

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

10 Citations (Scopus)


In this paper, we propose a new algorithm for constrained clustering, in which a new regularizer elegantly incorporates a small amount of weakly supervisory information in the form of pair-wise constraints to regularize the similarity between the low-dimensional representations of a set of data samples. By exploring both the local and global structures of the data samples with the guidance of the supervisory information, the proposed algorithm is capable of learning the lowdimensional representations with strong separability. Technically, the proposed algorithm is formulated and relaxed as a convex optimization model, which is further efficiently solved with the global convergence guaranteed. Experimental results on multiple benchmark data sets show that our proposed model can produce higher clustering accuracy than state-ofthe-art algorithms.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Multimedia and Expo (ICME)
Number of pages6
ISBN (Electronic)9781538617373
ISBN (Print)9781538617380
Publication statusPublished - Jul 2018
Externally publishedYes
Event2018 IEEE International Conference on Multimedia and Expo (ICME) - San Diego, United States
Duration: 23 Jul 201827 Jul 2018


Conference2018 IEEE International Conference on Multimedia and Expo (ICME)
Country/TerritoryUnited States
CitySan Diego


  • Constrained clustering
  • convex relaxation
  • weakly supervisory information


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