Adaptive Feature-Weighted Topological Manifold Graph Learning for Multi-View Data Clustering

Abstract

Existing graph-based multi-view clustering methods commonly employ view weights as global multipliers over both local graph construction and cross-view consensus graph learning. Such a design may over-penalize weakly aligned views, thereby hindering adaptive feature selection and degrading view-specific manifold preserving. In this paper, we propose to learn Adaptive Feature-weighted Topological Manifold graph for multi-view data Clustering (AF-TMC), which structurally decouples local feature-aware graph learning from global topological fusion. Specifically, AF-TMC restricts view weights to the topological manifold alignment term, while independently learns adaptive feature-weight matrices and affinity graphs for individual views. A unified consensus manifold graph and spectral embedding are then jointly optimized to capture shared cluster structures across multi-view representations. On model optimization, we devise an efficient block coordinate descent algorithm, where each subproblem admits a closed-form update or a tractable simplex projection. Comprehensive experiments on eight datasets verify the effectiveness of AF-TMC, which achieves strong overall performance against representative multi-view clustering methods. Analyses on convergence behavior, parameter sensitivity, consensus manifold graph visualization, and adaptive feature ranking substantiate the robustness and interpretability of AF-TMC.