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Publications in peer reviewed journals

1 Publication found
  • scikit-hubness: Hubness Reduction and Approximate Neighbor Search

    Feldbauer R, Rattei T, Flexer A
    2020 - The Journal of Open Source Software, 5: 1957


    scikit-hubness is a Python package for efficient nearest neighbor search in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality in nearest neighbor graphs. Specifically, it describes the increasing occurrence of hubs and antihubs with growing data dimensionality: Hubs are objects, that appear unexpectedly often among the nearest neighbors of others objects, while antihubs are never retrieved as neighbors. As a consequence, hubs may propagate their information (for example, class labels) too widely within the neighbor graph, while information from antihubs is depleted. These semantically distorted graphs can reduce learning performance in various tasks, such as classification, clustering, or visualization. Hubness is known to affect a variety of applied learning systems, or improper transport mode detection.

    Currently, there is a lack of fully-featured, up-to-date, user-friendly software dealing with hubness. Available packages miss critical features and have not been updated in years, or are not particularly user-friendly. In this paper we describe scikit-hubness, which provides powerful, readily available, and easy-to-use hubness-related methods.

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