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.

翻译：近年来，深度矩阵分解已成为无监督任务中的强大模型，取得了显著成果，尤其在多视图聚类领域。然而，现有方法通常缺乏有效的特征选择机制，并依赖于经验性的超参数选择。为解决这些问题，本文提出了一种新颖的用于多视图聚类的深度自适应加权矩阵分解方法（DMFAW）。该方法同时整合了特征选择并生成局部划分，从而提升了聚类效果。值得注意的是，特征权重由一个参数控制并调整，该参数通过受控制论启发的机制动态更新，这不仅提高了模型对不同数据集的稳定性和适应性，还加速了收敛过程。随后，本文提出了一种后期融合方法，将加权的局部划分与共识划分对齐。最后，通过一种具有理论收敛保证的交替优化算法求解该优化问题。在多个基准数据集上的大量实验表明，DMFAW在聚类性能方面优于当前最先进的方法。