Low-rank tucker decomposition for multi-view outlier detection based on meta-learning

Abstract

The analysis and mining of multi-view data have gained widespread attention, making multi-view anomaly detection a prominent research area. Despite notable advancements in the performance of existing multi-view anomaly detection methods, they still face certain limitations. (1) The existing methods fail to fully leverage the low-rank structure of multi-view data, which results in a lack of necessary interpretability when uncovering the latent relationships between views. (2) In the recovery of the consensus structure, the current methods achieve this merely through a simple aggregation process, lacking in-depth exploration and interaction between the potential structures of each view. To address these challenges, we propose the Low-Rank Tucker Decomposition based on Meta-Learning (LRTDM) for multi-view outlier detection. First, the low-rank Tucker decomposition is employed to reveal the low-rank structure of the multi-view self-expressive tensor. The factor matrices and core tensor effectively preserve and encode the latent structure of each view. This structured representation can efficiently capture the potential shared features between views, allowing for a more refined analysis of each individual view. Furthermore, meta-learning is utilized to define the learning and fusion of view-specific latent features as a nested optimization problem, which is solved alternately using a two-layer optimization scheme. Finally, anomalies are detected through the consensus matrix recovered from the latent representations and the error matrix during the self-expressive tensor learning process. Extensive experiments conducted on five publicly available datasets demonstrate the effectiveness of our approach. The results show that our algorithm improves detection accuracy by 2% to 10% compared to state-of-the-art methods.