scikit-hubness: Hubness Reduction and Approximate Neighbor Search

TL;DR

scikit-hubness is a Python package that addresses hubness in high-dimensional data, improving neighbor search, classification, and visualization tasks through analysis, reduction, and approximate search algorithms, integrated into scikit-learn.

Contribution

It introduces a comprehensive Python toolkit for hubness analysis, reduction, and approximate neighbor search, integrated with scikit-learn for easier adoption in machine learning workflows.

Findings

Provides algorithms for hubness analysis and reduction

Enables efficient approximate neighbor search in high dimensions

Integrated into scikit-learn for seamless use

Abstract

This paper introduces scikit-hubness, a Python package for efficient nearest neighbor search in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality, and is known to impair various learning tasks, including classification, clustering, and visualization. scikit-hubness provides algorithms for hubness analysis ("Is my data affected by hubness?"), hubness reduction ("How can we improve neighbor retrieval in high dimensions?"), and approximate neighbor search ("Does it work for large data sets?"). It is integrated into the scikit-learn environment, enabling rapid adoption by Python-based machine learning researchers and practitioners. Users will find all functionality of the scikit-learn neighbors package, plus additional support for transparent hubness reduction and approximate nearest neighbor search. scikit-hubness is developed using several quality assessment tools and principles, such as PEP8 compliance, unit tests with high code coverage, continuous integration on all major platforms (Linux, MacOS, Windows), and additional checks by LGTM. The source code is available at https://github.com/VarIr/scikit-hubness under the BSD 3-clause license. Install from the Python package index with $ pip install scikit-hubness.