An efficient method for autoencoder based outlier detection

Unsupervised Learning is widely used approach for outlier detection because non-availability of training dataset in various domains (especially, in evolving domains). Approaches like clustering-based, distance-based, density-based outlier detection methods have been proposed over the last several years. Recently, outlier detection using deep learning has drawn attention of researchers. Deep learning-based unsupervised techniques (autoencoder) minimize the reconstruction error using each data instance in the dataset and subsequently, data points with higher reconstruction error are treated as outlier points. However, autoencoder based model overestimates the reconstruction error for normal points whereas it is underestimated for outlier points. As a result, genuine outliers are missed by this approach.