Termin in Kalender importieren: iCal (Download)
Vorträge
| Vortragende(r)
|
Daniel Popovic
|
| Titel
|
High-Dimensional Neural-Based Outlier Detection
|
| 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.
|
- Neuen Vortrag erstellen
Hinweise
