Unsupervised Anomaly Detection Combining PCA and Neural Gases

Anomaly detection concerns the identification of patterns that deviate from normality in a dataset. It is a significant and common problem in a number of fields, and for this reason, a variety of methodologies, both standard and attributable to artificial intelligence, have been developed. The problem is complex due to the extreme variety of dataset and anomalies characteristics. In this paper, a new unsupervised approach to anomaly detection is presented. The algorithm is based on the combination of Principal Component Analysis and Neural Gases which, through the suitable vector quantization of the data, allows the calculation of an effective anomaly score. The method has been tested on numerous benchmarking datasets, reporting excellent results and outperforming other state-of-the-art algorithms for anomaly detection tasks.