My SP reaches 93-94% accuracy at best and does not use a kNN classifier. I experimented with kNN classifiers but found that they used a lot of memory and took a long to time to run, to the extent where kNN was not feasible to use. Instead my classifier keeps track of which neurons correlate with which input categories.
As for Numenta’s MNIST classifiers, I do not know how they work.
I’m assuming you are refering to the overlap between SP mini-column patterns from different inputs in the same category. The statistical classifier which I use will actually recognise many different patterns as the same category, and within a category different inputs will not yield a 95% overlap in output. If the SP had 95% overlap within all categories that would mean that it had learned viewpoint invariance…