动态调整特征权重，提升多视图聚类的准确性与稳定性。
Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering
- 用控制理论思想自适应更新特征权重，避免人工调参。
- 在多个基准数据集上优于现有最先进方法。
- 适合需要自动特征选择的多视图聚类任务。
深度矩阵分解已成为无监督任务中强有力的模型，尤其在多视图聚类中表现优异。然而，现有方法常缺乏有效的特征选择机制，且依赖经验性超参数设置。为此，我们提出一种新型多视图聚类深度矩阵分解自适应加权方法（DMFAW）。该方法同时整合特征选择与局部聚类生成，通过受控制理论启发的动态机制调节特征权重，不仅提升了模型在不同数据集上的稳定性和适应性，还加速了收敛。随后采用后融合策略将加权局部聚类结果对齐至共识聚类。优化问题通过具有理论收敛保证的交替优化算法求解。大量实验表明，DMFAW在多个基准数据集上均优于当前最优方法。
原文摘要 · Abstract (English)
Recently, deep matrix factorization has been established as a powerful model for unsupervised tasks, achieving promising results, especially for multi-view clustering. However, existing methods often lack effective feature selection mechanisms and rely on empirical hyperparameter selection. To address these issues, we introduce a novel Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering (DMFAW). Our method simultaneously incorporates feature selection and generates local partitions, enhancing clustering results. Notably, the features weights are controlled and adjusted by a parameter that is dynamically updated using Control Theory inspired mechanism, which not only improves the model's stability and adaptability to diverse datasets but also accelerates convergence. A late fusion approach is then proposed to align the weighted local partitions with the consensus partition. Finally, the optimization problem is solved via an alternating optimization algorithm with theoretically guaranteed convergence. Extensive experiments on benchmark datasets highlight that DMFAW outperforms state-of-the-art methods in terms of clustering performance.
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