Global and Local Scaling Reduce Hubs in Space

November 5, 2013:
NOTE: An updated software package for hubness analysis is available at our project homepage:

July 31, 2012

This is the main evaluation script to re-run the whole evaluation of the work submitted to JMLR. Matlab is needed to run the scripts

Download (72MB)

The following datasets are included in the download


To run extract the files in Start Matlab and use eval_mld('*') to start the evaluation. Note that this takes about a day to complete. If the script is called with the second parameter set to true, eval_mld('*', 1) the (heavy to compute) Goodman-Kruskal Index will be included in computation.

To evaluate a single database use desired collection as a parameter: eval_mld('corel-corel1000.db');

Then the Matlab output will look like:

Collection: corel1000 (n=1000)
size: 1000, classes: 10, dim: 192, intrinsic dim: 9
  Original (l_2)              - S^{k=1}: 1.83, C^{k=1}: 70.7%
                                S^{k=5}: 1.45, C^{k=5}: 65.2%
                                S^{k=20}: 1.52, C^{k=20}: 63.9%
                                SYMM^{k=5}: 35.8%, SYMM^{k=10%}: 42.1%

  NICDM                       - S^{k=1}: 1.00, C^{k=1}: 72.9%
                                S^{k=5}: 0.39, C^{k=5}: 72.0%
                                S^{k=20}: 0.63, C^{k=20}: 72.3%
                                SYMM^{k=5}: 69.8%, SYMM^{k=10%}: 70.0%

  MP (Empiric)                - S^{k=1}: 0.83, C^{k=1}: 71.6%
                                S^{k=5}: 0.31, C^{k=5}: 70.3%
                                S^{k=20}: 0.05, C^{k=20}: 69.0%
                                SYMM^{k=5}: 64.0%, SYMM^{k=10%}: 69.2%

As in the paper, S^{k=5} refers to the hubness, C^{k=1,5} to the classification accuracies. SYMM^{k=5,10%} to the percentage of symmetric nearest neighbor relations.

Mutual Proximity

The Mutal Proximity function is called norm_mp_empiric() (in file norm/norm_mp_empiric.m) and can be used with any distance matrix.

Implemented variants of MP are:


, Last Update: July 31, 2012