Anomaly Detection with Selective Dictionary Learning
Denis C. Ilie-Ablachim Bogdan Dumitrescu
In this paper we present new methods of anomaly detection based on Dictionary
Learning (DL) and Kernel Dictionary Learning (KDL). The main contribution
consists in the adaption of known DL and KDL algorithms in the form of
unsupervised methods, used for outlier detection. We propose a reduced kernel
version (RKDL), which is useful for problems with large data sets, due to the
large kernel matrix. We also improve the DL and RKDL methods by the use of a
random selection of signals, which aims to eliminate the outliers from the
training procedure. All our algorithms are introduced in an anomaly detection
toolbox and are compared to standard benchmark results.