What’s the difference between novelty detection and outlier detection?

In novelty detection, you have a dataset that contains only good data and you are trying to check if new observations are similar to the the good data. In other words, our goal is to check if new observations are outliers.

In outlier detection, you dataset may already have outliers and your goal is to identify such outliers.

Both novelty detection and outlier detection are used to detect anomalies.

Outlier detection is an unsupervised anomaly detection algorithm.

Novelty detection is a semi-supervised anomaly detection algorithm.

Outlier detection methods available in scikit-learn (LOF does not have a decision boundary as it does not have a predict method when used as an outlier detection algorithm)

Novelty detection concept

References:

• https://scikit-learn.org/stable/modules/outlier_detection.html
• https://www.linuxjournal.com/content/novelty-and-outlier-detection
• https://scikit-learn.org/dev/auto_examples/neighbors/plot_lof_novelty_detection.html