Adaptive Variational Autoencoders for Outlier Detection in Data Streams
|Termin||Fr 29. März 2019|
|Kurzfassung||Outlier detection targets at the discovery of abnormal data patterns. Adaptive Variational Autoencoders (AVA) are a novel approach for unsupervised outlier detection in data streams.
We show that they outperform recent state-of-the-art approaches on several datasets in a static as well as in a streaming setting. AVA exceeds the competition regarding streams with concept drift and an evolving feature space.
Furthermore, we propose a new approach to automatically construct an appropriate autoencoder network architecture in a completely unsupervised manner only parametrized by the dimensionality of the incoming stream.
We show via benchmarking that AVA is able to handle infinite data streams and fulfils all requirements for efficient stream processing such as constant memory consumption and constant processing times per point.