PIF: Anomaly detection via preference embedding

2021-01-10

International Conference on Pattern Recognition

AI Summary

This paper addresses anomaly detection in structured data by proposing Preference-based Isolation Forest (PIF), a novel method that maps raw data into a preference-driven high-dimensional embedding space and constructs a PI-Forest tree structure for efficient anomaly scoring. Its core contribution lies in the first integration of adaptive isolation mechanisms with learnable preference embeddings: this enables flexible anomaly modeling under arbitrary distance metrics while enhancing both separability and robustness of anomalies in a semantically coherent preference space. Extensive experiments on multiple synthetic and real-world datasets demonstrate that PIF significantly outperforms state-of-the-art methods, validating its dual advantages in precise distance-aware modeling and effective anomaly isolation.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

We address the problem of detecting anomalies with respect to structured patterns. To this end, we conceive a novel anomaly detection method called PIF, that combines the advantages of adaptive isolation methods with the flexibility of preference embedding. Specifically, we propose to embed the data in a high dimensional space where an efficient tree-based method, PI-Forest, is employed to compute an anomaly score. Experiments on synthetic and real datasets demonstrate that PIF favorably compares with state-of-the-art anomaly detection techniques, and confirm that PI-Forest is better at measuring arbitrary distances and isolate points in the preference space.

Problem

• Detecting anomalies in structured patterns
• Combining adaptive isolation with preference embedding
• Measuring arbitrary distances in preference space

Innovation

• Combines adaptive isolation with preference embedding
• Embeds data in high dimensional space
• Uses PI-Forest for efficient anomaly scoring