Currently I am looking for some method to do novelty and outlier detection. I found some good example here using scikit-learn (Link1). However, it is based on supervised learning and I believe the idea behind is the 'One Class SVM'. Therefore, the method requires the training data is not polluted by outliers.
However, I have got some data that contaminated by outliers already. I can't separate a clean dataset for training purpose. I am wondering if there is an unsupervised learning based method to detect the outliers?
For example, my data can be fitted using Mixture of Gaussian model, but before applying GMM, I would like to get rid of the outliers.
Thanks very much. A.