Github user Yunni commented on a diff in the pull request:
https://github.com/apache/spark/pull/15795#discussion_r86877678
--- Diff: docs/ml-features.md ---
@@ -1396,3 +1396,134 @@ for more details on the API.
{% include_example python/ml/chisq_selector_example.py %}
</div>
</div>
+
+# Locality Sensitive Hashing
+[Locality Sensitive
Hashing(LSH)](https://en.wikipedia.org/wiki/Locality-sensitive_hashing) is a
class of dimension reduction hash families, which can be used as both feature
transformation and machine-learned ranking. Difference distance metric has its
own LSH family class in `spark.ml`, which can transform feature columns to hash
values as new columns. Besides feature transforming, `spark.ml` also
implemented approximate nearest neighbor algorithm and approximate similarity
join algorithm using LSH.
+
+In this section, we call a pair of input features a false positive if the
two features are hashed into the same hash bucket but they are far away in
distance, and we define false negative as the pair of features when their
distance are close but they are not in the same hash bucket.
+
+## Random Projection for Euclidean Distance
+**Note:** Please note that this is different than the [Random Projection
for cosine
distance](https://en.wikipedia.org/wiki/Locality-sensitive_hashing#Random_projection).
--- End diff --
No, that is tracked in #18082.
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