Deep multi-sphere support vector data description

Abstract

Deep learning is increasingly used for unsupervised feature extraction and anomaly detection in big datasets. Most deep learning based anomaly detection techniques separately train a neural network for feature extraction, then apply a traditional anomaly detection method on the extracted features. These hybrid techniques have achieved higher accuracy than traditional anomaly detection methods and reconstruction-error-based deep autoencoders. However, recent research demonstrates that jointly optimising the objectives of the deep network and the anomaly detection technique in a hybrid architecture substantially improves detection performance. Existing methods that use this objective assume th..