Neural-Based Outlier Detection in Data Streams

Aus SDQ-Institutsseminar
Vortragende(r) Florian Pieper
Vortragstyp Proposal
Betreuer(in) Edouard Fouché
Termin Fr 19. Januar 2018
Kurzfassung Outlier detection often needs to be done unsupervised with high dimensional data in data streams. “Deep structured energy-based models” (DSEBM) and “Variational Denoising Autoencoder” (VDA) are two promising approaches for outlier detection. They will be implemented and adapted for usage in data streams. Finally, their performance will be shown in experiments including the comparison with state of the art approaches.