Self-organising map was learned using the SOM
Toolbox^{1} for
Matlab.
I implemented the soft reconstruction with Matlab.
The number of model vectors and the width of the softening kernels
associated with them were left as parameters and the selection of
values is not studied herein. Instead, the values with the best
results were used. Number of iterations was the default value in the
SOM toolbox and additional 50 iterations through all the data were run with
neighbourhood set off.

I modified the NFA code for Matlab to support missing values.
The code needed to reproduce the experiments will be publicly available^{2}.
The NFA method was tried with fixed number of hidden neurons using several different number of factors only, because
the algorithm requires about ten thousand batch iterations, where one batch iteration means going through all the observations once.