通过变分自编码器循环置换，解决多视图数据缺失下的表征不一致问题。
Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs
- 用随机置换变量实现跨视图生成，保持潜在空间对应关系。
- 在7个数据集上验证，不同缺失率下聚类与生成性能均优于基线。
- 适合处理视图不完整、需挖掘视图间不变关联的场景。
多视图表示学习旨在利用各视图间的共享与互补信息，从多视图数据中提取统一表征。然而，当视图存在不规则缺失时，不完整数据会导致表征缺乏充分性与一致性。为此，我们提出多视图变分自编码器置换方法（MVP），挖掘不完整数据中视图间的不变关系。MVP在变分自编码器的潜在空间中建立视图间对应关系，实现缺失视图的推断，并聚合更充分的信息。为获得有效的证据下界（ELBO），我们对变量进行随机置换以实现跨视图生成，再按视图划分，确保置换下语义不变。此外，引入基于循环置换后验分布的信息先验，将正则化项转化为分布间的相似性度量，增强一致性。我们在7个具有不同缺失率的多样化数据集上验证了该方法的有效性，在多视图聚类和生成任务中表现优异。
原文摘要 · Abstract (English)
Multi-View Representation Learning (MVRL) aims to derive a unified representation from multi-view data by leveraging shared and complementary information across views. However, when views are irregularly missing, the incomplete data can lead to representations that lack sufficiency and consistency. To address this, we propose Multi-View Permutation of Variational Auto-Encoders (MVP), which excavates invariant relationships between views in incomplete data. MVP establishes inter-view correspondences in the latent space of Variational Auto-Encoders, enabling the inference of missing views and the aggregation of more sufficient information. To derive a valid Evidence Lower Bound (ELBO) for learning, we apply permutations to randomly reorder variables for cross-view generation and then partition them by views to maintain invariant meanings under permutations. Additionally, we enhance consistency by introducing an informational prior with cyclic permutations of posteriors, which turns the regularization term into a similarity measure across distributions. We demonstrate the effectiveness of our approach on seven diverse datasets with varying missing ratios, achieving superior performance in multi-view clustering and generation tasks.
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