Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models

Yaniv Shulman

Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models

Machine Learning 2019-04-02 v1 Machine Learning

Authors: Yaniv Shulman

Abstract

A method for unsupervised contextual anomaly detection is proposed using a cross-linked pair of Variational Auto-Encoders for assigning a normality score to an observation. The method enables a distinct separation of contextual from behavioral attributes and is robust to the presence of anomalous or novel contextual attributes. The method can be trained with data sets that contain anomalies without any special pre-processing.

Keywords

change point detection variational autoencoders semi-supervised learning

Cite

@article{arxiv.1904.00548,
  title  = {Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models},
  author = {Yaniv Shulman},
  journal= {arXiv preprint arXiv:1904.00548},
  year   = {2019}
}

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