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

Unsupervised anomaly detection with autoencoders is effective because anomalies reconstruct differently from a well-regularized latent space. Real sensor data are often skewed and non-Gaussian, rendering mean-based estimators unreliable. Reconstruction error via Euclidean distance overlooks correlation structure in the latent space, weakening detection of near anomalies with similar feature distributions. We propose a correlation-aware latent distance that better preserves informative structure, improving anomaly detection on skewed data.