Several books that I have read do not distinguish the several models that exist for anomaly and outlier detection.
After I read about these models, I have chosen to detect anomalous events on unsupervised and supervised data the following algorithms:
- One class support vector machine
- Support vector machine
Let's imagine that these models were really good at detecting anomalous points. Now I want to detect outliers and novel events. Can I use the same algorithms? If so, in what sense it differs using anomaly, outlier, and novelty detection models?
In outlier and novelty detection I need to remove anomalous points before applying a model?