Multi-view representation learning (MvRL) has garnered substantial attention in recent years, driven by the increasing demand for applications that can effectively process and analyze data from multiple sources. In this context, graph Laplacian-based MvRL methods have demonstrated remarkable success in representing multi-view data. However, these methods often struggle with generalization to new data and face challenges with scalability. Moreover, in many practical scenarios, multi-view data is contaminated by noise or outliers. In such cases, modern deep-learning-based MvRL approaches that rely on alignment or contrastive objectives can lead to misleading results, as they may impose incorrect consistency between clear and corrupted data sources. We introduce $\textit{SpecRaGE}$, a novel fusion-based framework that integrates the strengths of graph Laplacian methods with the power of deep learning to overcome these challenges. SpecRage uses neural networks to learn parametric mapping that approximates a joint diagonalization of graph Laplacians. This solution bypasses the need for alignment while enabling generalizable and scalable learning of informative and meaningful representations. Moreover, it incorporates a meta-learning fusion module that dynamically adapts to data quality, ensuring robustness against outliers and noisy views. Our extensive experiments demonstrate that SpecRaGE outperforms state-of-the-art methods, particularly in scenarios with data contamination, paving the way for more reliable and efficient multi-view learning. Our code will be made publicly available upon acceptance.

翻译：近年来，多视图表示学习（MvRL）因其在处理和分析多源数据应用中的需求日益增长而受到广泛关注。在此背景下，基于图拉普拉斯算子的MvRL方法在表示多视图数据方面取得了显著成功。然而，这些方法往往难以泛化到新数据，并面临可扩展性挑战。此外，在许多实际场景中，多视图数据会受到噪声或异常值的污染。在这种情况下，依赖对齐或对比目标的现代基于深度学习的MvRL方法可能导致误导性结果，因为它们可能在清晰数据源与污染数据源之间施加错误的一致性。我们提出$\textit{SpecRaGE}$，一种新颖的基于融合的框架，它整合了图拉普拉斯方法的优势与深度学习的能力，以克服这些挑战。SpecRaGE使用神经网络学习参数化映射，以近似图拉普拉斯算子的联合对角化。该解决方案绕过了对齐需求，同时实现了信息丰富且有意义表示的可泛化和可扩展学习。此外，它引入了一个元学习融合模块，能够动态适应数据质量，确保对异常值和噪声视图的鲁棒性。我们的大量实验表明，SpecRaGE在性能上超越了现有最先进方法，尤其在数据污染场景中，为更可靠、高效的多视图学习开辟了道路。我们的代码将在论文被接受后公开提供。