Population Anomaly Detection through Deep Gaussianization

TL;DR

This paper presents a novel deep learning approach using adversarial autoencoders for detecting population anomalies in high-dimensional data, applicable to various real-world scenarios like fraud and disease outbreaks.

Contribution

It introduces a new algorithm leveraging deep Gaussianization for population anomaly detection, capable of identifying distribution shifts in complex data sets.

Findings

Effective detection of population anomalies demonstrated across multiple domains

Quantitative results show improved accuracy over existing methods

Qualitative insights into anomaly characteristics obtained

Abstract

We introduce an algorithmic method for population anomaly detection based on gaussianization through an adversarial autoencoder. This method is applicable to detection of `soft' anomalies in arbitrarily distributed highly-dimensional data. A soft, or population, anomaly is characterized by a shift in the distribution of the data set, where certain elements appear with higher probability than anticipated. Such anomalies must be detected by considering a sufficiently large sample set rather than a single sample. Applications include, but not limited to, payment fraud trends, data exfiltration, disease clusters and epidemics, and social unrests. We evaluate the method on several domains and obtain both quantitative results and qualitative insights.