A Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data

arXiv:2301.00462v3 Announce Type: replace
Abstract: Unsupervised learning-based anomaly detection in latent space has gained importance since discriminating anomalies from normal data becomes difficult in high-dimensional space. Both density estimation and distance-based methods to detect anomalies in latent space have been explored in the past. These methods prove that retaining valuable properties of input data in latent space helps in the better reconstruction of test data. Moreover, real-world sensor data is skewed and non-Gaussian in nature, making mean-based estimators unreliable for …

abstract anomaly anomaly detection arxiv autoencoder correlation cs.lg data detection importance normal prove space type unsupervised unsupervised learning