Adaptive Variational Autoencoders for Outlier Detection in Data Streams
|Termin||Fr 29. März 2019|
|Kurzfassung||Outlier detection targets the discovery of abnormal data patterns. Typical scenarios are intrusion, fraud detection and predictive maintenance.
Since in real-world settings the data is often available as an infinite and ever evolving stream, in this thesis we propose Adaptive Variational Autoencoders (AVA) as a novel approach for unsupervised outlier detection in data streams.
First, we introduce a streaming framework for training arbitrary generative models on data streams. Generative models are used for sample generation to keep learned knowledge.
Second, we instanciate this framework with a Variational Autoencoder as AVA. To handle data streams in a truely unsupervised manner, AVA adapts its network architecture to 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.