Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection

Abstract

Unsupervised anomaly detection from high dimensional data like mobility networks is a challenging task. Study of different approaches of feature engineering from such high dimensional data have been a focus of research in this field. This study aims to investigate the transferability of features learned by network classification to unsupervised anomaly detection. We propose use of an auxiliary classification task to extract features from unlabelled data by supervised learning, which can be used for unsupervised anomaly detection. We validate this approach by designing experiments to detect anomalies in mobility network data from New York and Taipei, and compare the results to traditional unsupervised feature learning approaches of PCA and autoencoders. We find that our feature learning approach yields best anomaly detection performance for both datasets, outperforming other studied approaches. This establishes the utility of this approach to feature engineering, which can be applied to other problems of similar nature.

References in corpus (9)

• Semi-Supervised Classification with Graph Convolutional Networks
• Quantifying the benefits of vehicle pooling with shareability networks
• Geo-located Twitter as the proxy for global mobility patterns
• Efficient GAN-Based Anomaly Detection
• Exploring universal patterns in human home-work commuting from mobile phone data
• Deep Structured Energy Based Models for Anomaly Detection
• A General Optimization Technique for High Quality Community Detection in Complex Networks
• Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
• Pattern and Anomaly Detection in Urban Temporal Networks