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Autoencoder-based cleaning of non-categorical data in probabilistic databases

Nijweide, F.P.J. (2020) Autoencoder-based cleaning of non-categorical data in probabilistic databases.

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Abstract:This report investigates the use of autoencoders to remove noise from non-categorical data in probabilistic databases. Previous research has shown that this is possible for categorical data, but a new solution is needed to do this for continuous or discrete distributions. The approach chosen was to approximate the data using discrete sampling. After training the autoencoder, we measured the difference between "cleaned" data and the original data using the Jensen-Shannon divergence. We concluded that the most effective solution was to use semi-supervised learning. This solution is quite effective at low sampling densities, reducing 99.54% of noise in a probabilistic database, while its performance at higher sampling densities is slightly lower, leading to an 86.99% reduction in the amount of noise.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
Link to this item:http://purl.utwente.nl/essays/82344
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