With the development of multi-view learning, multi-view outlier detection has received increasing attention in recent years. However, the current research still faces two challenges: (1) The current research lacks theoretical analysis tools for multi-view outliers. (2) Most current multi-view outlier detection algorithms are based on shallow structural assumptions of the data, such as cluster assumptions and subspace assumptions, thus they are not suitable for more complex data distributions. In addressing these two issues, this article proposes three occurrence mechanisms of multi-view outlier, which serve as foundational theoretical analysis tools for multi-view outliers. Utilizing proposed mechanisms, we analyze the impact of multi-view outliers and the information structure of multi-view data and validate our findings through experiments. Finally, we propose a novel algorithm referred to as Information-Aware Multi-View Outlier Detection (IAMOD). In contrast to other methods, IAMOD focuses on the information structure of multi-view data without relying on shallow structural assumptions. By learning a compact representation of the sample that is semantically rich and non-redundant, IAMOD can accurately identify multi-view outliers by comparing the consistency of the representations’ neighbors and views. Extensive experimental results demonstrate that our approach outperforms several state-of-the-art multi-view outlier detection methods.

In recent years, federated learning has achieved remarkable results in the medical and financial fields, but various attacks have always plagued federated learning. Data poisoning attack and defense research in horizontal federated learning are sufficient, yet vertical federated data poisoning attack and defense remains an open area due to two challenges: (1) Complex data distributions lead to immense attack possibilities, and (2) defense methods are insufficient for complex data distributions. We have discovered that from the perspective of information theory, the above challenges can be addressed elegantly and succinctly with a solution. We first reveal the information-theoretic mechanisms underlying vertical federated data poisoning attacks and then propose an unsupervised vertical federated data poisoning defense method (VFedAD) based on information theory. VFedAD learns semantic-rich client data representations through contrastive learning task and cross-client prediction task to identify anomalies. Experiments show VFedAD effectively detects vertical federated anomalies, protecting subsequent algorithms from vertical federated data poisoning attacks.

With the rapidly growing attention to multi-view data in recent years, multi-view outlier detection has become a rising field with intense research. These researches have made some success, but still exist some issues that need to be solved. First, many multi-view outlier detection methods can only handle datasets that conform to the cluster structure but are powerless for complex data distributions such as manifold structures. This overly restrictive data assumption limits the applicability of these methods. In addition, almost the majority of multi-view outlier detection algorithms cannot solve the online detection problem of multi-view outliers. To address these issues, we propose a new detection method based on the local similarity relation and data reconstruction, i.e., the Self-Representation Method with Local Similarity Preserving for fast multi-view outlier detection (SRLSP). By using the local similarity structure, the proposed method fully utilizes the characteristics of outliers and detects outliers with an applicable objective function. Besides, a well-designed optimization algorithm is proposed, which completes each iteration with linear time complexity and can calculate each instance parallelly. Also, the optimization algorithm can be easily extended to the online version, which is more suitable for practical production environments. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed method on both performance and time complexity.