无需调参的多视图聚类方法，自动筛选关键特征与视图。
Parameter-free entropy-regularized multi-view clustering with hierarchical feature selection
- 用熵正则化替代人工参数，实现跨视图自适应共识
- 通过信噪比加权自动优化特征与视图贡献，收敛有保障
- 支持层次化降维，适合高维异构数据聚类任务
多视图聚类在发现异构数据中的模式时面临挑战，需处理高维特征并剔除无关信息。传统方法依赖人工调参且缺乏一致的跨视图融合机制。本文提出两种互补算法：AMVFCM-U 和 AAMVFCM-U，构建统一的无参框架。通过熵正则化项替代模糊化参数，强制实现自适应跨视图一致性。核心创新在于基于信噪比的正则化（δ_j^h = x̄_j^h / (σ_j^h)^2），实现可证明的特征加权，并结合双层熵项自动平衡视图与特征贡献。AAMVFCM-U 进一步引入层次化降维，通过自适应阈值（θ^{h^{(t)}} = d_h^{(t)} / n）在特征与视图层面进行降维。在五个不同基准上的评估表明，该方法优于15种先进方法。AAMVFCM-U 最高实现97%的计算效率提升，维度降至原始大小的0.45%，并能自动识别最优视图组合以发现关键模式。
原文摘要 · Abstract (English)
Multi-view clustering faces critical challenges in automatically discovering patterns across heterogeneous data while managing high-dimensional features and eliminating irrelevant information. Traditional approaches suffer from manual parameter tuning and lack principled cross-view integration mechanisms. This work introduces two complementary algorithms: AMVFCM-U and AAMVFCM-U, providing a unified parameter-free framework. Our approach replaces fuzzification parameters with entropy regularization terms that enforce adaptive cross-view consensus. The core innovation employs signal-to-noise ratio based regularization ($δ_j^h = \frac{\bar{x}_j^h}{(σ_j^h)^2}$) for principled feature weighting with convergence guarantees, coupled with dual-level entropy terms that automatically balance view and feature contributions. AAMVFCM-U extends this with hierarchical dimensionality reduction operating at feature and view levels through adaptive thresholding ($θ^{h^{(t)}} = \frac{d_h^{(t)}}{n}$). Evaluation across five diverse benchmarks demonstrates superiority over 15 state-of-the-art methods. AAMVFCM-U achieves up to 97% computational efficiency gains, reduces dimensionality to 0.45% of original size, and automatically identifies critical view combinations for optimal pattern discovery.
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