There exists multiple novelty detection methods. I'll discuss two:
- One-class SVM
Both of them have parameters. For example, the SVM has a $\nu$ parameter and if the SVM uses the RBF kernel, it has a smoothing parameter. The LOF has the parameter $k$.
To get good results, these parameters have to be tuned. However, in novelty detection, you only have one dataset which is normal on which the models are trained. Now in papers I see that the models are then tested on nonnormal data and the parameters are tuned to get a good prediction. However, then the training process uses both normal and nonnormal data which is not desired (if we assume tuning part of the training phase).
I am in the situation were I only have only normal data. How can I tune the hyperparameters? Is this just a limitation of the methods?
Maybe models with automatic tuning would resolve the issue.