A collection of no-nonsense GMLVQ demo code
A (hopefully) easy-to-use collection of 'no-nonsense' demo code
in Matlab (TM) is available for Generalized Matrix Relevance Learning (GMLVQ).
The zip-archives contain implementations of batch gradient
descent training with automated step size control for
variants of GMLVQ, procedures for validation and discriminative visualiation, a brief documentation, and example data sets.
It is recommended to use the latest version only, elder versions are provided for completeness and reproducibility of earlier results.
Feel free to use the code for getting acquainted with the method, but please do
cite the link and papers, if appropriate.
In any case, do not use the code for critical applications and make sure to read the disclaimer.
In the very likely case that you should come across
bugs or would like to comment or suggest improvements, please
let me know.
Do not expect much of the code in terms of readability and efficiency.
If you would like to keep receiving update notifications, please send an e-mail to firstname.lastname@example.org
Also note that a more complete and more sophisticated Matlab (TM) implementation of GMLVQ and its variants is available here.
(**) Averages of the prototypes over validation runs (run_L1O or run_validation) were flawed. Essentially, the 'mean prototypes' were stretched by a factor of 2, approximately. All other aspects of the code, training and validation performances etc. were not affected. However, the graphics showing the averaged prototypes displayed stretched versions, unfortunately. Learning curves, averaged matrix etc. were not affected.