Structural Outlier

Structural outliers represent unusual data points deviating significantly from the underlying data structure or manifold, posing challenges across diverse fields. Current research focuses on developing robust methods for detecting these outliers, employing techniques like generative models (e.g., VAEs, normalizing flows), manifold learning, and information retrieval algorithms to identify anomalies in high-dimensional data, including images, graphs, and sensor networks. Effective outlier detection is crucial for improving the reliability and robustness of machine learning models, enhancing data quality in various applications, and facilitating scientific discoveries in fields like astronomy and particle physics.