Semi-supervised maximum margin clustering with pairwise constraints

Hong Zeng*, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)

Abstract

The pairwise constraints specifying whether a pair of samples should be grouped together or not have been successfully incorporated into the conventional clustering methods such as k-means and spectral clustering for the performance enhancement. Nevertheless, the issue of pairwise constraints has not been well studied in the recently proposed maximum margin clustering (MMC), which extends the maximum margin framework in supervised learning for clustering and often shows a promising performance. This paper therefore proposes a pairwise constrained MMC algorithm. Based on the maximum margin idea in MMC, we propose a set of effective loss functions for discouraging the violation of given pairwise constraints. For the resulting optimization problem, we show that the original nonconvex problem in our approach can be decomposed into a sequence of convex quadratic program problems via constrained concave-convex procedure (CCCP). Subsequently, we present an efficient subgradient projection optimization method to solve each convex problem in the CCCP sequence. Experiments on a number of real-world data sets show that the proposed constrained MMC algorithm is scalable and outperforms the existing constrained MMC approach as well as the typical semi-supervised clustering counterparts.

Original languageEnglish
Article number5728817
Pages (from-to)926-939
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume24
Issue number5
DOIs
Publication statusPublished - 2012

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • constrained concave-convex procedure.
  • maximum margin clustering
  • pairwise constraints
  • Semi-supervised clustering

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