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        "description": ".. -*- mode: rst -*-\n\n|Travis|_ |AppVeyor|_ |Codecov|_ |CircleCI|_ |ReadTheDocs|_\n\n.. |Travis| image:: https://travis-ci.com/FlopsKa/gmd.svg?branch=master\n.. _Travis: https://travis-ci.com/FlopsKa/gmd\n\n.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/84j8gekk5ob3i28d/branch/master?svg=true\n.. _AppVeyor: https://ci.appveyor.com/project/FlopsKa/gmd/\n\n.. |Codecov| image:: https://codecov.io/gh/FlopsKa/gmd/branch/master/graph/badge.svg\n.. _Codecov: https://codecov.io/gh/FlopsKa/gmd\n\n.. |CircleCI| image:: https://circleci.com/gh/FlopsKa/gmd.svg?style=shield&circle-token=:circle-token\n.. _CircleCI: https://circleci.com/gh/FlopsKa/gmd/tree/master\n\n.. |ReadTheDocs| image:: https://readthedocs.org/projects/gmd/badge/?version=latest\n.. _ReadTheDocs: https://gmd.readthedocs.io/en/latest/?badge=latest\n\nScikit-learn Greedy Maximum Deviation (GMD) Algorithm\n=====================================================\n\n.. _scikit-learn: https://scikit-learn.org\n\nThis project provides a `scikit-learn`_ compatible python implementation of the\nalgorithm presented in [`Trittenbach2018`_] together with some usage examples\nand a reproduction of the results from the paper.\n\nRecent approaches in outlier detection seperate the subspace search from the\nactual outlier detection and run the outlier detection algorithm on a\nprojection of the original feature space. See [`Keller2012`_]. As a result the\ndetection algorithm (Local Outlier Factor is used in the paper) does not suffer\nfrom the curse of dimensionality.\n\n\n.. _Trittenbach2018: https://link.springer.com/article/10.1007/s41060-018-0137-7\n.. _Keller2012: https://ieeexplore.ieee.org/document/6228154\n\n.. _documentation: https://gmd.readthedocs.io/en/latest/\n\nRefer to the documentation_ to see usage examples.",
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