Dual-constrained semi-supervised concept factorization for multiview clustering

Abstract

Multi-view clustering aims to integrate complementary information from diverse views; however, effectively improving clustering performance with limited supervision remains a significant challenge. Existing semi-supervised methods often fail to fully exploit sparse labeled data or lack effective mechanisms to propagate supervisory constraints across views. To address these limitations, we propose a novel semi-supervised multi-view clustering method based on concept factorization, termed DSCF. Specifically, DSCF establishes a unified framework that incorporates pointwise constraints to enforce clustering consistency for labeled samples, while a hypergraph-based pairwise constraint propagation strategy is employed to transfer supervisory information to unlabeled data. Furthermore, a cross-view iterative update scheme is designed to effectively align and fuse constraint information among different views. Extensive experiments on eight benchmark multi-view datasets demonstrate that DSCF consistently outperforms several relevant multi-view clustering methods, validating its effectiveness in leveraging limited labeled information to enhance clustering performance.