Pairwise Constraint Propagation With Dual Adversarial Manifold Regularization

Yuheng JIA, Hui LIU, Junhui HOU, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

19 Citations (Scopus)


Pairwise constraints (PCs) composed of must-links (MLs) and cannot-links (CLs) are widely used in many semisupervised tasks. Due to the limited number of PCs, pairwise constraint propagation (PCP) has been proposed to augment them. However, the existing PCP algorithms only adopt a single matrix to contain all the information, which overlooks the differences between the two types of links such that the discriminability of the propagated PCs is compromised. To this end, this article proposes a novel PCP model via dual adversarial manifold regularization to fully explore the potential of the limited initial PCs. Specifically, we propagate MLs and CLs with two separated variables, called similarity and dissimilarity matrices, under the guidance of the graph structure constructed from data samples. At the same time, the adversarial relationship between the two matrices is taken into consideration. The proposed model is formulated as a nonnegative constrained minimization problem, which can be efficiently solved with convergence theoretically guaranteed. We conduct extensive experiments to evaluate the proposed model, including propagation effectiveness and applications on constrained clustering and metric learning, all of which validate the superior performance of our model to state-of-the-art PCP models.
Original languageEnglish
Pages (from-to)5575-5587
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number12
Early online date24 Feb 2020
Publication statusPublished - Dec 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the Natural Science Foundation of China under Grants 61871342, 61772344, 61672443 and in part by Hong Kong RGC General Research Funds 9042820 (CityU 11219019), 9042489 (CityU 11206317), 9042322 (CityU 11200116), 9042816 (CityU 11209819) and 9048123 (CityU 21211518).


  • Adversarial relationship
  • manifold regularization
  • pairwise constraint propagation (PCP)
  • semisupervised


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