Hashing for Structure-Based Anomaly Detection

被引:0
作者
Leveni, Filippo [1 ]
Magri, Luca [1 ]
Alippi, Cesare [1 ,2 ]
Boracchi, Giacomo [1 ]
机构
[1] Politecn Milano DEIB, Milan, Italy
[2] Univ Svizzera Italiana, Lugano, Switzerland
来源
IMAGE ANALYSIS AND PROCESSING, ICIAP 2023, PT II | 2023年 / 14234卷
关键词
D O I
10.1007/978-3-031-43153-1_3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:25 / 36
页数:12
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