正则化对比部分多视图异常检测
Regularized Contrastive Partial Multi-view Outlier Detection
AI总结：
针对现有多视图异常检测方法抗异常干扰弱、邻域适应性差且不适用于部分多视图数据的问题，提出RCPMOD方法，通过三类对比与正则化损失及跨视图关系迁移补全缺失视图，在四个基准数据集上性能超越现有最优方法。
摘要：
近年来，多视图异常检测（MVOD）方法取得了显著进展，其目标是识别多视图数据集中的异常样本。该领域的一个关键要点是更好地检测仅存在于多视图数据中的类别异常和类别属性异常。然而，现有方法要么在学习视图一致性信息时无法降低异常样本的影响，要么在邻域结构各异的场景中表现不佳。此外，大多数方法都不适用于现实场景中的部分多视图数据。为克服这些缺陷，我们提出了一种名为正则化对比部分多视图异常检测（RCPMOD）的新方法。在该框架中，我们利用对比学习来学习视图一致性信息，并通过一致性程度区分异常样本。具体而言，我们提出：（1）基于理论分析的启发，设计带有潜在异常样本记忆库的异常感知对比损失，以消除异常样本带来的偏差；（2）邻域对齐对比损失，用于捕捉视图共享的局部结构相关性；（3）扩散正则化损失，防止模型对异常样本过拟合。借助跨视图关系迁移技术，我们能够基于邻域特征轻松补全缺失的视图样本。在四个基准数据集上的实验结果表明，我们提出的方法在不同设置下均优于当前最优的同类方法。
英文摘要：
In recent years, multi-view outlier detection (MVOD) methods have advanced significantly, aiming to identify outliers within multi-view datasets. A key point is to better detect class outliers and class-attribute outliers, which only exist in multi-view data. However, existing methods either is not able to reduce the impact of outliers when learning view-consistent information, or struggle in cases with varying neighborhood structures. Moreover, most of them do not apply to partial multi-view data in real-world scenarios. To overcome these drawbacks, we propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD). In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Specifically, we propose (1) An outlier-aware contrastive loss with a potential outlier memory bank to eliminate their bias motivated by a theoretical analysis. (2) A neighbor alignment contrastive loss to capture the view-shared local structural correlation. (3) A spreading regularization loss to prevent the model from overfitting over outliers. With the Cross-view Relation Transfer technique, we could easily impute the missing view samples based on the features of neighbors. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors under different settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
