semvec/pypi-packages huggingface dataset:
gmd 0.0.1
A scikit-learn compatible python/cython implementation of the GMD algorithm.
License: MIT

.. -*- mode: rst -*-

|Travis|_ |AppVeyor|_ |Codecov|_ |CircleCI|_ |ReadTheDocs|_

.. |Travis| image:: https://travis-ci.com/FlopsKa/gmd.svg?branch=master
.. _Travis: https://travis-ci.com/FlopsKa/gmd

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.. _AppVeyor: https://ci.appveyor.com/project/FlopsKa/gmd/

.. |Codecov| image:: https://codecov.io/gh/FlopsKa/gmd/branch/master/graph/badge.svg
.. _Codecov: https://codecov.io/gh/FlopsKa/gmd

.. |CircleCI| image:: https://circleci.com/gh/FlopsKa/gmd.svg?style=shield&circle-token=:circle-token
.. _CircleCI: https://circleci.com/gh/FlopsKa/gmd/tree/master

.. |ReadTheDocs| image:: https://readthedocs.org/projects/gmd/badge/?version=latest
.. _ReadTheDocs: https://gmd.readthedocs.io/en/latest/?badge=latest

Scikit-learn Greedy Maximum Deviation (GMD) Algorithm
=====================================================

.. _scikit-learn: https://scikit-learn.org

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.


.. _Trittenbach2018: https://link.springer.com/article/10.1007/s41060-018-0137-7
.. _Keller2012: https://ieeexplore.ieee.org/document/6228154

.. _documentation: https://gmd.readthedocs.io/en/latest/

Refer to the documentation_ to see usage examples.