  In this paper, we present two pipelines in order to reduce the feature space
for anomaly detection using the One Class SVM. As a first stage of both
pipelines, we compare the performance of three convolutional autoencoders. We
use the PCA method together with t-SNE as the first pipeline and the
reconstruction errors based method as the second. Both methods have potential
for the anomaly detection, but the reconstruction error metrics prove to be
more robust for this task. We show that the convolutional autoencoder
architecture doesn't have a significant effect for this task and we prove the
potential of our approach on the real world dataset.
