What is Novelty Detection / Anomaly Detection

Novelty Detection and Anomaly Detection are machine learning techniques used to identify data points that significantly deviate from expected patterns. They play a crucial role in various applications such as fraud detection, network security, medical diagnosis, and industrial monitoring.

Technically, Novelty Detection refers to detecting new, unlabelled samples in the context of known normal data, whereas Anomaly Detection focuses on identifying abnormal data points that deviate from normal behavior patterns. The primary difference lies in the fact that Novelty Detection deals with unseen normal data, while Anomaly Detection is based on labeled data.

The impact of these techniques is profound as they enable organizations to monitor and respond to potential risks and issues in real-time, thereby reducing losses and enhancing efficiency. With the surge in data volume, the importance of Novelty Detection and Anomaly Detection is increasing, and they may integrate further with deep learning and big data technologies to improve detection accuracy and efficiency.

However, these techniques also face challenges in practice. Defining what constitutes 'normal' in complex data environments and effectively detecting in high-dimensional data are areas that require further research. Additionally, the training and validation of models require substantial labeled data, which may not be readily available in certain domains.