Anomaly Detection with Selective Dictionary Learning
This work addresses anomaly detection problems for data analysts, but it is incremental as it adapts existing methods with minor improvements.
The paper tackled anomaly detection by adapting Dictionary Learning and Kernel Dictionary Learning into unsupervised methods for outlier detection, proposing a reduced kernel version for large datasets and improving algorithms with random signal selection to exclude outliers during training, with results compared to standard benchmarks.
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.