LAG-Net: A Deep Unfolding Network for Multi-View Clustering via Learnable Anchor Graph

Abstract

Multi-view clustering aims to discover consistent cluster structures from heterogeneous features without supervision. Anchor-based methods improve scalability by representing samples through a compact set of anchors, but fixed anchors may be misaligned with the evolving cluster geometry. This mismatch is the main problem addressed here: the shared sample–anchor graph and the global anchor geometry need to be refined together across heterogeneous views, rather than in two disconnected stages. This paper proposes Learnable Anchor Graph Network (LAG-Net), a deep unfolding framework that jointly learns a shared anchor graph, view-specific anchor indicators, and global anchor alignment within a unified model. The global anchor alignment provides geometric guidance for shared anchor graph learning and promotes anchor consistency across heterogeneous views. By unfolding the derived optimization procedure into a trainable network, LAG-Net enables the anchor representations and sample–anchor relationships to be progressively refined. Experiments on six benchmark datasets demonstrate the effectiveness and scalability of the proposed method compared with representative multi-view clustering approaches.