Correntropy-Guided Tensor Graph Learning for Robust Semi-Supervised Multi-View Clustering

Abstract

Multi-view clustering has emerged as a significant research direction in the information age, as multiple feature representations become increasingly available. However, traditional multi-view clustering methods are often unsupervised and fail to exploit available label information. In practice, fully labeled data are scarce, while partially labeled data are more common and can significantly improve clustering performance. Moreover, real-world data are frequently corrupted by noise or outliers. To address these challenges, this paper proposes a Correntropy-guided Matrix Factorization and Tensor Graph Learning (CMTGL) framework for robust semi-supervised multi-view clustering. Specifically, CMTGL incorporates partial label information into a shared low-dimensional representation through constrained low-rank matrix factorization and employs the maximum correntropy criterion (MCC) to reduce the influence of noisy samples and outliers. In addition, view-specific graphs are stacked into a third-order tensor and regularized by the tensor Schatten p-norm to exploit high-order correlations across multiple views. A consensus graph is jointly learned to capture the common structural information shared among different views. The resulting optimization problem is efficiently solved by a block coordinate descent algorithm with an extrapolation accelerated block coordinate update (BCU) scheme. Extensive experiments on six public benchmark datasets demonstrate that the proposed method achieves superior clustering performance compared to state-of-the-art approaches.