Wang Sen, Chen Lian, Liang Zhijian, Liu Qingyang
School of Science, East China Jiaotong University, Nanchang 330013, China.
Sensors (Basel). 2025 Apr 28;25(9):2778. doi: 10.3390/s25092778.
Noises and outliers often degrade the final prediction performance in practical data processing. Multi-view learning by integrating complementary information across heterogeneous modalities has become one of the core techniques in the field of machine learning. However, existing methods rely on explicit-view clustering and stringent alignment assumptions, which affect the effectiveness in addressing the challenges such as inconsistencies between views, noise interference, and misalignment across different views. To alleviate these issues, we present a latent multi-view representation learning model based on low-rank embedding by implicitly uncovering the latent consistency structure of data, which allows us to achieve robust and efficient multi-view feature fusion. In particular, we utilize low-rank constraints to construct a unified latent subspace representation and introduce an adaptive noise suppression mechanism that significantly enhances robustness against outliers and noise interference. Moreover, the Augmented Lagrangian Multiplier Alternating Direction Minimization (ALM-ADM) framework enables efficient optimization of the proposed method. Experimental results on multiple benchmark datasets demonstrate that the proposed approach outperforms existing state-of-the-art methods in both clustering performance and robustness.
在实际数据处理中，噪声和离群值常常会降低最终的预测性能。通过整合异构模态中的互补信息进行多视图学习已成为机器学习领域的核心技术之一。然而，现有方法依赖于显式视图聚类和严格的对齐假设，这影响了应对视图间不一致、噪声干扰以及不同视图间未对齐等挑战的有效性。为缓解这些问题，我们提出了一种基于低秩嵌入的潜在多视图表示学习模型，通过隐式揭示数据的潜在一致性结构，使我们能够实现强大且高效的多视图特征融合。具体而言，我们利用低秩约束来构建统一的潜在子空间表示，并引入自适应噪声抑制机制，显著增强了对离群值和噪声干扰的鲁棒性。此外，增广拉格朗日乘子交替方向最小化（ALM - ADM）框架实现了所提方法的高效优化。在多个基准数据集上的实验结果表明，所提方法在聚类性能和鲁棒性方面均优于现有最先进的方法。
