Semisupervised Affinity Matrix Learning via Dual-Channel Information Recovery

Yuheng JIA, Hui LIU, Junhui HOU, Sam KWONG, Qingfu ZHANG

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

15 Citations (Scopus)


This article explores the problem of semisupervised affinity matrix learning, that is, learning an affinity matrix of data samples under the supervision of a small number of pairwise constraints (PCs). By observing that both the matrix encoding PCs, called pairwise constraint matrix (PCM) and the empirically constructed affinity matrix (EAM), express the similarity between samples, we assume that both of them are generated from a latent affinity matrix (LAM) that can depict the ideal pairwise relation between samples. Specifically, the PCM can be thought of as a partial observation of the LAM, while the EAM is a fully observed one but corrupted with noise/outliers. To this end, we innovatively cast the semisupervised affinity matrix learning as the recovery of the LAM guided by the PCM and EAM, which is technically formulated as a convex optimization problem. We also provide an efficient algorithm for solving the resulting model numerically. Extensive experiments on benchmark datasets demonstrate the significant superiority of our method over state-of-the-art ones when used for constrained clustering and dimensionality reduction. The code is publicly available at
Original languageEnglish
Pages (from-to)7919-7930
JournalIEEE Transactions on Cybernetics
Issue number8
Early online date8 Jan 2021
Publication statusPublished - Aug 2022
Externally publishedYes


  • Clustering
  • graph learning
  • semisupervised learning


Dive into the research topics of 'Semisupervised Affinity Matrix Learning via Dual-Channel Information Recovery'. Together they form a unique fingerprint.

Cite this