Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/15795#discussion_r86744459
--- 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).
+
+[Random
Projection](https://en.wikipedia.org/wiki/Locality-sensitive_hashing#Stable_distributions)
is the LSH family in `spark.ml` for Euclidean distance. The Euclidean distance
is defined as follows:
+`\[
+d(\mathbf{x}, \mathbf{y}) = \sqrt{\sum_i (x_i - y_i)^2}
+\]`
+Its LSH family projects features onto a random unit vector and divide the
projected results to hash buckets:
+`\[
+h(\mathbf{x}) = \lfloor \frac{\mathbf{x} \cdot \mathbf{v}}{r} \rfloor
+\]`
+where `v` is a normalized random unit vector and `r` is user-defined
bucket length.
+
+The input features in Euclidean space are represented in vectors. Both
sparse and dense vectors are supported.
+
+The bucket length can be used to trade off the performance of random
projection. A larger bucket lowers the false negative rate but usually
increases running time and false positive rate.
--- End diff --
Isn't the point here that near vectors end up in nearby buckets? I feel
like this is skipping the intuition of why you would care about this technique
or when you'd use it. Not like this needs a whole paragraph, just a few
sentences of pointers?
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