Feature Space Reduction As Data Preprocessing For The Anomaly Detection
Feature Space Reduction As Data Preprocessing For The Anomaly Detection
In this paper, we present two pipelines in order to reduce the feature space for anomalydetection using the One Class SVM. As a first stage of both pipelines, we compare the performanceof three convolutional autoencoders. We use the PCA method together with t-SNE as the first pipelineand the reconstruction errors based method as the second. Both methods have potential for theanomaly detection, but the reconstruction error metrics prove to be more robust for this task. Weshow that the convolutional autoencoder architecture doesn’t have a significant effect for this task andwe prove the potential of our approach on the real world dataset.
- Brno University of Technology Czech Republic
PCA, OC-SVM, Convolutional autoencoder, Anomaly detection, t-SNE, CNN
PCA, OC-SVM, Convolutional autoencoder, Anomaly detection, t-SNE, CNN
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