Say I'm using scikits implementation of Local Outlier Factor with euclidean distance being used by the reachability function. My input features are magnitudes apart, so is it advisable that I normalize my input features before training the function even though there might be several separate clusters.
Solved – Should you normalize your training data for Local Outlier Factor
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Best Answer
That would be the recommended approach since Local Outlier Factors are based on Nearest Neighbor approach which is a similarity based algorithm. Normalization is recommended for most cases where similarity measures are used, unless you'd want high magnitude features to dominate the distance calculation. If you have a mixture of continuous, ordinal and binary variables in there, you may want to evaluate a few different scales for them.