Anomaly detection: Difference between revisions

Anomaly detection often faces the problem of high dimensionality, where data may contain many features or variables that make it challenging to detect anomalies and visualize them. To address this challenge, feature selection, dimensionality reduction techniques or visualization strategies can be employed in order to simplify the data and focus on the most pertinent ones.

Another difficulty is concept drift, in which the distribution of data alters over time and makes a model outdated or ineffective at detecting new anomalies. To combat this problem, adaptive or online learning techniques such as reinforcement learning should be utilized that update models in real-time or adapt to changes in data distribution.