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 that the normal (i.e., non-anomalous) data comes from a single distribution.In this paper, we show that violation of this assumption negatively affects performance of these methods and creates model bias in the favour of anomalies.We propose Deep Multi-sphere Support Vector Data Description, which jointly optimises the objectives of the deep network and anomaly detection.It generates useful and discriminative features by embeding normal data with a multi-modal distribution into multiple data-enclosing hyperspheres with minimum volume.We empirically show that our proposed method outperforms state-of-the-art shallow and deep anomaly detection methods.

Topics & Concepts