Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/4304#discussion_r29733205
--- Diff: docs/mllib-dimensionality-reduction.md ---
@@ -157,6 +157,23 @@ val pc: Matrix = mat.computePrincipalComponents(10) //
Principal components are
// Project the rows to the linear space spanned by the top 10 principal
components.
val projected: RowMatrix = mat.multiply(pc)
{% endhighlight %}
+
+The following code demonstrates how to compute principal components on
source vectors
+and use them to project the vectors into a low-dimensional space while
keeping associated labels:
+
+{% highlight scala %}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.feature.PCA
+
+val data: RDD[LabeledPoint] = ...
+
+// Compute the top 10 principal components.
+val pca = new PCA(10).fit(data.map(_.features))
+
+// Project vectors to the linear space spanned by the top 10 principal
components, keeping the labe
--- End diff --
typo: "labe" --> "label"
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