A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text

Despite the maturity of anomaly detection in structured and image data, anomaly detection in text remains surprisingly under-explored. Existing methods struggle with two key challenges: manifold collapse in high-dimensional language embeddings, and semantic entanglement that obscures contextual anomalies. We address both with the Robust Local AutoEncoder (RLAE), which combines robust subspace recovery with local geometry preservation. It allows to learn disentangled and anomaly-sensitive representations. We extend this to RoSAE, an ensemble of randomly pruned RLAEs that enhances robustness through architectural diversity. We introduce TAC which is the first benchmark framework to distinguish anomaly types in text, using topic hierarchies to separate independent from contextual anomalies for fair and reproducible evaluation. TAC resolves inconsistencies in prior benchmarks. RoSAE consistently outperforms state-of-the-art baselines across eight diverse corpora (6 more than state-of-the-art approaches), particularly under challenging contextual settings. Our results highlight the importance of both local structure and ensemble diversity in textual anomaly detection, and position RoSAE as a robust, scalable, and efficient method for tackling both independent and contextual anomalies.