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.
- Operating System:
- Data Formats:
High Dimensional Data Analysis,