Hub-toolbox

Latest version: v2.5.2

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2.5.2

The Hub Toolbox now supports approximate hubness reduction for very large data sets. Please check out the `approximate` module for hubness reduction with linear time and space complexity.

2.3.1

None

2.3.0

The HUB TOOLBOX now supports the following hubness reduction methods:
- Mutual Proximity (Empiric, Gauss, Indep. Gauss, Indep. Gamma)
- Local Scaling (Normal, NICDM)
- Shared Nearest Neighbors
- Centering (Normal, Weighted, Localized)
- DisSim (Global, Local)

Most methods now support (sparse) similarity matrices.

Most methods now support train/test splits.

MP and hubness functions now support parallel processing.

Performance improvements for several methods.

Vast unit test coverage.

2.2

Features (experimental) centering functions for hubness reduction.

2.1.0

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