Institutsseminar/2017-10-06
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Datum | Fr 6. Oktober 2017, 11:30 Uhr | |
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Dauer | 45 min | |
Ort | Raum 348 (Gebäude 50.34) | |
Webkonferenz | ||
Vorheriger Termin | Fr 29. September 2017 | |
Nächster Termin | Fr 13. Oktober 2017 |
Vorträge
Vortragende(r) | Daniel Popovic | |
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Titel | High-Dimensional Neural-Based Outlier Detection | |
Vortragstyp | Diplomarbeit | |
Betreuer(in) | Edouard Fouché | |
Vortragsmodus | ||
Kurzfassung | Outlier detection in high-dimensional spaces is a challenging task because of consequences of the curse of dimensionality. Neural networks have recently gained in popularity for a wide range of applications due to the availability of computational power and large training data sets. Several studies examine the application of different neural network models, such an autoencoder, self-organising maps and restricted Boltzmann machines, for outlier detection in mainly low-dimensional data sets. In this diploma thesis we investigate if these neural network models can scale to high-dimensional spaces, adapt the useful neural network-based algorithms to the task of high-dimensional outlier detection, examine data-driven parameter selection strategies for these algorithms, develop suitable outlier score metrics for these models and investigate the possibility of identifying the outlying dimensions for detected outliers. |
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