High-Dimensional Neural-Based Outlier Detection: Unterschied zwischen den Versionen

Aktuelle Version vom 7. September 2017, 15:13 Uhr

Vortragende(r)	Daniel Popovic
Vortragstyp	Diplomarbeit
Betreuer(in)	Edouard Fouché
Termin	Fr 6. Oktober 2017
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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 |betreuer=Edouard Fouché
 |termin=Institutsseminar/2017-10-06
-|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 subspace for detected outliers.
+|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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