Multiview alignment, achieving one-to-one correspondence of multiview inputs, is critical in many real-world multiview applications, especially for cross-view data analysis problems. An increasing amount of work has studied this alignment problem with canonical correlation analysis (CCA). However, existing CCA models are prone to misalign the multiple views due to either the neglect of uncertainty or the inconsistent encoding of the multiple views. To tackle these two issues, this letter studies multiview alignment from a Bayesian perspective. Delving into the impairments of inconsistent encodings, we propose to recover correspondence of the multiview inputs by matching the marginalization of the joint distribution of multiview random variables under different forms of factorization. To realize our design, we present adversarial CCA (ACCA), which achieves consistent latent encodings by matching the marginalized latent encodings through the adversarial training paradigm. Our analysis, based on conditional mutual information, reveals that ACCA is flexible for handling implicit distributions. Extensive experiments on correlation analysis and cross-view generation under noisy input settings demonstrate the superiority of our model.

中文翻译：

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通过一致的潜在编码在 CCA 中进行多视图对齐和生成

多视图对齐，实现多视图输入的一一对应，在许多现实世界的多视图应用中至关重要，尤其是对于跨视图数据分析问题。越来越多的工作已经通过典型相关分析 (CCA) 研究了这个对齐问题。然而，由于忽视不确定性或多个视图的编码不一致，现有的 CCA 模型容易使多个视图错位。为了解决这两个问题，这封信从贝叶斯的角度研究了多视图对齐。深入研究不一致编码的缺陷，我们建议通过匹配不同形式分解下多视图随机变量联合分布的边缘化来恢复多视图输入的对应关系。为了实现我们的设计，我们提出了对抗性 CCA (ACCA)，它通过对抗训练范式匹配边缘化的潜在编码来实现一致的潜在编码。我们基于条件互信息的分析表明，ACCA 可以灵活地处理隐式分布。在嘈杂的输入设置下进行的相关分析和交叉视图生成的大量实验证明了我们模型的优越性。