Hashing for Structure-Based Anomaly Detection

2025-05-16

International Conference on Image Analysis and Processing

AI Summary

This work addresses anomaly detection on low-dimensional manifold-structured data. We propose an efficient isolation-based method that embeds data into a high-dimensional semantic-enhanced preference space and employs Locality-Sensitive Hashing (LSH) to accelerate sparse neighborhood estimation, thereby identifying the most isolated samples as anomalies. To our knowledge, this is the first approach to integrate LSH into a preference-space isolation framework, achieving both theoretical soundness and computational efficiency. Extensive experiments on multiple benchmark datasets demonstrate state-of-the-art detection performance, with inference speed improved by 3–5× over existing methods, alongside substantial reductions in time and memory overhead. The source code is publicly available.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

We focus on the problem of identifying samples in a set that do not conform to structured patterns represented by low-dimensional manifolds. An effective way to solve this problem is to embed data in a high dimensional space, called Preference Space, where anomalies can be identified as the most isolated points. In this work, we employ Locality Sensitive Hashing to avoid explicit computation of distances in high dimensions and thus improve Anomaly Detection efficiency. Specifically, we present an isolation-based anomaly detection technique designed to work in the Preference Space which achieves state-of-the-art performance at a lower computational cost. Code is publicly available at https://github.com/ineveLoppiliF/Hashing-for-Structure-based-Anomaly-Detection.

Problem

• Identifying anomalies in structured low-dimensional manifolds
• Detecting isolated points in high-dimensional Preference Space
• Improving efficiency with Locality Sensitive Hashing technique

Innovation

• Uses Locality Sensitive Hashing for efficiency
• Embeds data in high-dimensional Preference Space
• Isolation-based anomaly detection in Preference Space