Adaptive Variational Autoencoders for Outlier Detection in Data Streams: Unterschied zwischen den Versionen

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|betreuer=Edouard Fouché
 
|betreuer=Edouard Fouché
 
|termin=Institutsseminar/2019-03-29
 
|termin=Institutsseminar/2019-03-29
|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.
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|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.
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.
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First, we present a streaming framework for arbitrary generative models such as Variational Autoencoders (VAE), which we name Adaptive Generative Networks (AGN).
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The unique property of a generative model is its ability of generating samples from the model.
  
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.
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Second, we introduce Adaptive Variational Autoencoders (AVA) for unsupervised outlier detection in data streams.
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Adaptive Variational Autoencoders use the AGN framework with a Variational Autoencoder as generative model and automatically adapt the model to an occurring concept drift.
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Furthermore, they generate their network architecture based on the dimensionality of incoming data to further improve the unsupervised outlier detection experience.
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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.
 
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Version vom 25. März 2019, 19:56 Uhr

Vortragende(r) Florian Pieper
Vortragstyp Masterarbeit
Betreuer(in) Edouard Fouché
Termin Fr 29. März 2019
Vortragsmodus
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 arbitrary generative models such as Variational Autoencoders (VAE), which we name Adaptive Generative Networks (AGN). 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 use the AGN framework with a Variational Autoencoder as generative model and automatically adapt the model to an occurring concept drift. Furthermore, they generate their network architecture based on the dimensionality of incoming data to further improve the unsupervised outlier detection experience.

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.