Eduardo, Simão

Nazábal, Alfredo

Sutton, Charles

Williams, Christopher K I

English

We focus on the problem of unsupervised cell outlier detection and repair in mixed-type tabular data. Traditional methods are concerned only on detecting which rows in the dataset are outliers. However, identifying which cells corrupt a specific row is an important problem in practice, and the very first step towards repairing them. We introduce the Robust Variational Autoencoder (RVAE), a deep generative model that learns the joint distribution of the clean data while identifying the outlier cells, allowing their imputation (repair). RVAE explicitly learns the probability of each cell being an outlier, balancing different likelihood models in the row outlier score, making the method suitable for OD in mixed-type datasets. We show experimentally that not only RVAE performs better than several state-of-the-art methods in cell OD and repair for tabular data, but also that is robust against the initial hyper-parameter selection

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Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data

https://www.research.ed.ac.uk/portal/files/142691221/Robust_Variational_Autoencoders_EDUARDO_DOA17012020_AFV.pdf

Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data

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