Bilateral Graph Filtering Framework with Alternating Optimization for Robust Multi-View Outlier Detection

Multi-view outlier detection is essential for analyzing complex data, yet many existing approaches rely on shallow fusion schemes or remain sensitive to noise. We propose a Bilateral Graph Filtering Framework with Alternating Optimization for Robust Multi-View Outlier Detection(BGFMOD), which detects attribute outliers via high-frequency residuals and identifies class outliers through cross-view structural inconsistencies. An alternating optimization strategy efficiently addresses the non-convexity of bilateral filtering while preserving spectral orthogonality. Experiments on multiple benchmark datasets show that BGFMOD achieves superior AUC performance and strong robustness across diverse multi-view scenarios. The complete code is available at https://github.com/Jinzhao11/BGFMOD