Outlier Detection

Outlier detection aims to identify data points deviating significantly from the norm within a dataset, a crucial task across diverse fields. Current research emphasizes developing robust algorithms that handle high-dimensionality, varying cluster shapes, and the challenges of unsupervised learning, with approaches ranging from ensemble methods and graph-based techniques to generative models and vision-language models. These advancements improve accuracy and interpretability, particularly in applications like recommender systems, anomaly detection in images and time series, and ensuring the reliability of machine learning models in safety-critical domains. The ongoing focus is on addressing algorithmic bias, enhancing explainability, and developing efficient methods for large-scale datasets.