Feature space reduction as data preprocessing for the anomaly detection
This work addresses anomaly detection in data preprocessing, but it is incremental as it builds on existing methods like PCA, t-SNE, and autoencoders without introducing major innovations.
The paper tackles feature space reduction for anomaly detection using One Class SVM, comparing two pipelines with convolutional autoencoders and finding that reconstruction error metrics are more robust, though the autoencoder architecture has little effect, as demonstrated on a real-world dataset.
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.