Github user Yunni commented on the issue:
https://github.com/apache/spark/pull/15800
> One way to look at it is that (a) will contain many duplicates in the L
sets of points, so (b) is more likely to have higher precision and recall.
I think this might be the place we are not on the same page. I consider the
output of (a)/(b) as our "Probing Sequence" (or "Probing buckets"), and in the
next step we pick and return k nearest keys in those buckets. Do you agree with
this part?
If you agree, then I claim more duplicates (It's actually redundancy rather
than duplicates) brings more chance for finding the correct k nearest neighbors
because we enlarge our search range.
If you disagree, I think we are not discussing based on the same NN search
implementation (differs from the current implementation). I would like to know
how you return k nearest neighbor after (b)?
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