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. An Adaptive Variational Autoencoder (AVA) is a novel approach for unsupervised outlier detection in data streams.
First, we present a streaming framework for training arbitrary generative models such as Variational Autoencoders (VAE), which we name Adaptive Generative Networks (AGN), in data streams. The unique property of a generative model is its ability of generating samples from the model.
Second, we introduce Adaptive Variational Autoencoders (AVA) for unsupervised outlier detection in data streams. Adaptive Variational Autoencoders instantiate the AGN framework with a Variational Autoencoder as generative model and automatically adapt the model to an occurring concept drift. Furthermore, AVA generates its network architecture based on the dimensionality of incoming data.
Our experiments against several benchmark outlier data sets show that AVA outperforms the state of the art and successfully adapts to streams with concept drift.