you can always make a new vector that's orthogonal to all the ones currently used and see if the inclusion improves performance on your tasks
> see if the inclusion improves performance on your tasks
Apparently it doesn't at least not in our models with our training applied to our tasks.
So if we expand one of those 3 things and notice that 17-th vector makes a difference then we are having progress.
If all need just 16 dimensions if we ever make one that needs 17 we know we are making progress instead of running in circles.