Anomaly Detection for High-Dimensional Data Using Large Deviations Principle

• URL: http://arxiv.org/abs/2109.13698v1
• Date: Tue, 28 Sep 2021 13:13:14 GMT
• Title: Anomaly Detection for High-Dimensional Data Using Large Deviations Principle
• Authors: Sreelekha Guggilam and Varun Chandola and Abani Patra
• Abstract summary: We propose an anomaly detection algorithm that can scale to high-dimensional data using concepts from the theory of large deviations. The proposed Large Deviations Anomaly Detection (LAD) algorithm is shown to outperform state of art anomaly detection methods on a variety of large and high-dimensional benchmark data sets.
• Score: 0.8526086056172273
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Most current anomaly detection methods suffer from the curse of dimensionality when dealing with high-dimensional data. We propose an anomaly detection algorithm that can scale to high-dimensional data using concepts from the theory of large deviations. The proposed Large Deviations Anomaly Detection (LAD) algorithm is shown to outperform state of art anomaly detection methods on a variety of large and high-dimensional benchmark data sets. Exploiting the ability of the algorithm to scale to high-dimensional data, we propose an online anomaly detection method to identify anomalies in a collection of multivariate time series. We demonstrate the applicability of the online algorithm in identifying counties in the United States with anomalous trends in terms of COVID-19 related cases and deaths. Several of the identified anomalous counties correlate with counties with documented poor response to the COVID pandemic.

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