Li Dan, Wang Haibao, Ying Shihui
IEEE Trans Neural Netw Learn Syst. 2025 Jul;36(7):13051-13065. doi: 10.1109/TNNLS.2024.3482408.
Integrating information from multiple views to obtain potential representations with stronger expressive ability has received significant attention in practical applications. Most existing algorithms usually focus on learning either the consistent or complementary representation of views and, subsequently, integrate one-to-one corresponding sample representations between views. Although these approaches yield effective results, they do not fully exploit the information available from multiple views, limiting the potential for further performance improvement. In this article, we propose an unsupervised multiview representation learning method based on sample relationships, which enables the one-to-many fusion of intraview and interview information. Due to the heterogeneity of views, we need mainly face the two following challenges: 1) the discrepancy in the dimensions of data across different views and 2) the characterization and utilization of sample relationships across these views. To address these two issues, we adopt two modules: the dimension consistency relationship enhancement module and the multiview graph learning module. Thereinto, the relationship enhancement module addresses the discrepancy in data dimensions across different views and dynamically selects data dimensions for each sample that bolsters intraview relationships. The multiview graph learning module devises a novel multiview adjacency matrix to capture both intraview and interview sample relationships. To achieve one-to-many fusion and obtain multiview representations, we employ the graph autoencoder structure. Furthermore, we extend the proposed architecture to the supervised case. We conduct extensive experiments on various real-world multiview datasets, focusing on clustering and multilabel classification tasks, to evaluate the effectiveness of our method. The results demonstrate that our approach significantly improves performance compared to existing methods, highlighting the potential of leveraging sample relationships for multiview representation learning. Our code is released at https://github.com/lilidan-orm/one-to-many-multiview on GitHub.
在实际应用中，整合来自多个视图的信息以获得具有更强表达能力的潜在表示受到了广泛关注。大多数现有算法通常专注于学习视图的一致表示或互补表示，随后整合视图之间一对一对应的样本表示。尽管这些方法取得了有效的结果，但它们没有充分利用来自多个视图的可用信息，限制了进一步提高性能的潜力。在本文中，我们提出了一种基于样本关系的无监督多视图表示学习方法，该方法能够实现视图内和视图间信息的一对多融合。由于视图的异质性，我们主要需要面对以下两个挑战：1）不同视图间数据维度的差异；2）这些视图间样本关系的表征与利用。为了解决这两个问题，我们采用了两个模块：维度一致性关系增强模块和多视图图学习模块。其中，关系增强模块解决不同视图间数据维度的差异问题，并为每个样本动态选择支持视图内关系的数据维度。多视图图学习模块设计了一种新颖的多视图邻接矩阵来捕捉视图内和视图间的样本关系。为了实现一对多融合并获得多视图表示，我们采用了图自动编码器结构。此外，我们将所提出的架构扩展到了有监督的情况。我们在各种真实世界的多视图数据集上进行了广泛的实验，重点关注聚类和多标签分类任务，以评估我们方法的有效性。结果表明，与现有方法相比，我们的方法显著提高了性能，突出了利用样本关系进行多视图表示学习的潜力。我们的代码已在GitHub上的https://github.com/lilidan-orm/one-to-many-multiview发布。
