Prototype-oriented hypergraph representation learning for anomaly detection in tabular data

Anomaly detection in tabular data holds significant importance across various industries such as manufacturing, healthcare, and finance. However, existing methods are constrained by the size and diversity of datasets, leading to poor generalization. Moreover, they primarily concentrate on feature correlations while overlooking interactions among data instances. Furthermore, the vulnerability of these methods to noisy data hinders their deployment in practical engineering applications. To tackle these issues, this paper proposes prototype-oriented hypergraph representation learning for anomaly detection in tabular data (PHAD). Specifically, PHAD employs a diffusion-based data augmentation strategy tailored for tabular data to enhance both the size and diversity of the training data. Subsequently, it constructs a hypergraph from the combined augmented and original training data to capture higher-order correlations among data instances by leveraging hypergraph neural networks. Lastly, PHAD utilizes an adaptive fusion of local and global data representations to derive the prototype of latent normal data, serving as a benchmark for detecting anomalies. Extensive experiments on twenty-six public datasets across various engineering fields demonstrate that our proposed PHAD outperforms other state-of-the-art methods in terms of performance, robustness, and efficiency.