About: Hubness-aware Machine Learning for High-dimensional Data

• BibTex support for all algorithm implementations, making all of them easy to reference (via algref package).

• Two more hubness-aware approaches (meta-metric-learning and feature construction)

• An implementation of Hit-Miss networks for analysis.

• Several minor bug fixes.

• The following instance selection methods were added: HMScore, Carving, Iterative Case Filtering, ENRBF.

• The following clustering quality indexes were added: Folkes-Mallows, Calinski-Harabasz, PBM, G+, Tau, Point-Biserial, Hubert's statistic, McClain-Rao, C-root-k.

• Some more experimental scripts have been included.

• Extensions in the estimation of hubness risk.

• Alias and weighted reservoir methods for weight-proportional random selection.