Information-aware Multi-view Outlier Detection

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.