Unsupervised Outlier Detection

Unsupervised outlier detection aims to identify data points deviating significantly from the norm without relying on pre-labeled examples, a crucial task across diverse fields. Current research emphasizes developing robust algorithms that address challenges like data bias, high dimensionality, and noisy labels, often employing deep learning models such as autoencoders and generative adversarial networks, as well as probabilistic methods like Dirichlet process mixtures. These advancements improve the accuracy and efficiency of outlier detection, impacting applications ranging from fraud detection and anomaly identification in autonomous driving to healthcare and social sciences by enabling more reliable analysis of complex datasets.