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

Abstract

Unsupervised learning-based anomaly detection using autoencoders has gained importance since anomalies behave differently than normal data when reconstructed from a well-regularized latent space. Existing research shows that retaining valuable properties of input data in latent space helps in the better reconstruction of unseen data. Moreover, real-world sensor data is skewed and non-Gaussian in nature rendering mean-based estimators unreliable for such cases. Reconstruction-based anomaly detection methods rely on Euclidean distance as the reconstruction error which does not consider useful correlation information in the latent space. In this work, we address some of the limitations of the Euclidean distance when used as a reconstruction error to detect anomalies (especially near anomalies) that have a similar distribution as the normal data in the feature space. We propose a latent dimension regularized autoencoder that leverages a robust form of the Mahalanobis distance (MD) to measure the latent space correlation to effectively detect near as well as far anomalies. We showcase that incorporating the correlation information in the form of robust MD in the latent space is quite helpful in separating both near and far anomalies in the reconstructed space.