A scikit-learn compatible python/cython implementation of the GMD algorithm.
Project description
Scikit-learn Greedy Maximum Deviation (GMD) Algorithm
This project provides a scikit-learn compatible python implementation of the algorithm presented in [Trittenbach2018] together with some usage examples and a reproduction of the results from the paper.
Recent approaches in outlier detection seperate the subspace search from the actual outlier detection and run the outlier detection algorithm on a projection of the original feature space. See [Keller2012]. As a result the detection algorithm (Local Outlier Factor is used in the paper) does not suffer from the curse of dimensionality.
Refer to the documentation to see usage examples.
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- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.2
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