Institutsseminar/2017-10-06
| Datum | Freitag, 6. Oktober 2017 | |
|---|---|---|
| Uhrzeit | 11:30 – 12:15 Uhr (Dauer: 45 min) | |
| Ort | Raum 348 (Gebäude 50.34) | |
| Prüfer/in | ||
| Webkonferenz | ||
| Vorheriger Termin | Fr 29. September 2017 | |
| Nächster Termin | Fr 13. Oktober 2017 |
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Vorträge
| Vortragende(r) | Daniel Popovic |
|---|---|
| Vortragstyp | Diplomarbeit |
| Betreuer(in) | Edouard Fouché |
| Vortragssprache | |
| 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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