Github user sethah commented on the issue:
https://github.com/apache/spark/pull/15800
Using this as hashing distance for near-neighbor search doesn't make sense
to me. If there aren't enough candidates where the distance is zero, we'll
select some candidates who have distance one. But these are just random
candidates since distance of one doesn't correspond to being similar at all, if
my understanding is correct. Does minhash really fit the abstraction of
multi-probing? I notice that they only use hyperplane projection method in
[this paper](http://www.cs.princeton.edu/cass/papers/mplsh_vldb07.pdf).
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