Dear scikit-learn Developers, My Name is Orges Leka and I would like to implement "Rapid Outlier Detection via Sampling" [1] in scikit-learn. In R this method is already available [2] by the authors of the method.
In Python I have not seen any implementation yet. The method is very simple yet effective as the authors show. First one selects say 20 points. Then computes the shortest distance of all other points to these 20 points. This is the outlier-score for one specific point. It would be nice to implement this with different metrics / distances (euclid, manhattan or other metrics) . How would I start the implementation? I have already git-cloned scikit-learn on my pc. Do I need to write object oriented or are functions also ok? If this succeeds, I would also like to extend the "example-outliers" doc with the above method. Kind regards Dipl. Math. Orges Leka [1] https://papers.nips.cc/paper/5127-rapid-distance-based-outlier-detection-via-sampling.pdf [2] https://github.com/mahito-sugiyama/sampling-outlier-detection
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