2 files changed, 72 insertions, 2 deletions
diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md
index 870fed6cc5..05f51168d8 100644
--- a/docs/mllib-dimensionality-reduction.md
+++ b/docs/mllib-dimensionality-reduction.md
@@ -137,7 +137,7 @@ statistical method to find a rotation such that the first coordinate has the lar
 possible, and each succeeding coordinate in turn has the largest variance possible. The columns of
 the rotation matrix are called principal components. PCA is used widely in dimensionality reduction.
 
-MLlib supports PCA for tall-and-skinny matrices stored in row-oriented format.
+MLlib supports PCA for tall-and-skinny matrices stored in row-oriented format and any Vectors.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
@@ -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 label
+val projected = data.map(p => p.copy(features = pca.transform(p.features)))
+{% endhighlight %}
+
 </div>
 
 <div data-lang="java" markdown="1">
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 03fedd0101..f723cd6b9d 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -507,7 +507,6 @@ v_N
 
 This example below demonstrates how to load a simple vectors file, extract a set of vectors, then transform those vectors using a transforming vector value.
 
-
 <div class="codetabs">
 <div data-lang="scala">
 {% highlight scala %}
@@ -531,3 +530,57 @@ val transformedData2 = parsedData.map(x => transformer.transform(x))
 </div>
 
 
+## PCA
+
+A feature transformer that projects vectors to a low-dimensional space using PCA.
+Details you can read at [dimensionality reduction](mllib-dimensionality-reduction.html).
+
+### Example
+
+The following code demonstrates how to compute principal components on a `Vector`
+and use them to project the vectors into a low-dimensional space while keeping associated labels
+for calculation a [Linear Regression]((mllib-linear-methods.html))
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.mllib.regression.LinearRegressionWithSGD
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.feature.PCA
+
+val data = sc.textFile("data/mllib/ridge-data/lpsa.data").map { line =>
+  val parts = line.split(',')
+  LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
+}.cache()
+
+val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
+val training = splits(0).cache()
+val test = splits(1)
+
+val pca = new PCA(training.first().features.size/2).fit(data.map(_.features))
+val training_pca = training.map(p => p.copy(features = pca.transform(p.features)))
+val test_pca = test.map(p => p.copy(features = pca.transform(p.features)))
+
+val numIterations = 100
+val model = LinearRegressionWithSGD.train(training, numIterations)
+val model_pca = LinearRegressionWithSGD.train(training_pca, numIterations)
+
+val valuesAndPreds = test.map { point =>
+  val score = model.predict(point.features)
+  (score, point.label)
+}
+
+val valuesAndPreds_pca = test_pca.map { point =>
+  val score = model_pca.predict(point.features)
+  (score, point.label)
+}
+
+val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean()
+val MSE_pca = valuesAndPreds_pca.map{case(v, p) => math.pow((v - p), 2)}.mean()
+
+println("Mean Squared Error = " + MSE)
+println("PCA Mean Squared Error = " + MSE_pca)
+{% endhighlight %}
+</div>
+</div>