  Autoencoders, as a dimensionality reduction technique, have been recently
applied to outlier detection. However, neural networks are known to be
vulnerable to overfitting, and therefore have limited potential in the
unsupervised outlier detection setting. Current approaches to ensemble-based
autoencoders do not generate a sufficient level of diversity to avoid the
overfitting issue. To overcome the aforementioned limitations we develop a
Boosting-based Autoencoder Ensemble approach (in short, BAE). BAE is an
unsupervised ensemble method that, similarly to the boosting approach, builds
an adaptive cascade of autoencoders to achieve improved and robust results. BAE
trains the autoencoder components sequentially by performing a weighted
sampling of the data, aimed at reducing the amount of outliers used during
training, and at injecting diversity in the ensemble. We perform extensive
experiments and show that the proposed methodology outperforms state-of-the-art
approaches under a variety of conditions.
