diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 4fe470a8de81..1d5286f70632 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -617,3 +617,43 @@ println("PCA Mean Squared Error = " + MSE_pca)
 {% endhighlight %}
 </div>
 </div>
+
+## Significant Selector
+Idea of this transformation it safe reduce big vector that was produced by Hashing TF for example
+for reduce requirement of memory for manipulation on them. 
+
+This transformation create a model that keep only indices that has different values on fit stage.
+
+### Example
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.feature.{HashingTF, SignificantSelector}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+
+val hashingTF = new HashingTF
+val localDocs: Seq[(Double, Array[String])] = Seq(
+  (1d, "a a b b b c d".split(" ")),
+  (0d, "a b c d a b c".split(" ")),
+  (1d, "c b a c b a a".split(" ")))
+
+val docs = sc.parallelize(localDocs, 2)
+
+val tf: RDD[LabeledPoint] = docs.map { case (label, words) => LabeledPoint(label, hashingTF.transform(words))}
+// scala> tf.first().features.size
+//  res4: Int = 1048576
+
+val transformer = new SignificantSelector().fit(tf.map(_.features))
+
+val transformed_tf = tf.map(p => p.copy(features = transformer.transform(p.features)))
+// scala> transformed_tf.first().features.size
+// res5: Int = 4
+
+// now you have smallest vector that has same features,
+// but request less memory for manipulation on DecisionTree for example
+{% endhighlight %}
+</div>
+</div>
+
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/SignificantSelector.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/SignificantSelector.scala
new file mode 100644
index 000000000000..6758b41acccd
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/SignificantSelector.scala
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.feature
+
+import scala.collection.mutable
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
+import org.apache.spark.rdd.RDD
+
+/**
+ * :: Experimental ::
+ * Model to extract significant indices from vector.
+ *
+ * Significant indices is vector's index that has different value for different vectors.
+ *
+ * For example, when you use HashingTF they create big sparse vector,
+ * and this code convert to smallest vector that don't include same values indices for all vectors.
+ *
+ * @param indices array of significant indices.
+ */
+@Experimental
+class SignificantSelectorModel(val indices: Array[Int]) extends VectorTransformer {
+
+  /**
+   * Applies transformation on a vector.
+   *
+   * @param vector vector to be transformed.
+   * @return transformed vector.
+   */
+  override def transform(vector: Vector): Vector = vector match {
+    case DenseVector(vs) =>
+      Vectors.dense(indices.map(vs))
+
+    case SparseVector(s, ids, vs) =>
+      var sv_idx = 0
+      var new_idx = 0
+      val elements = new mutable.ListBuffer[(Int, Double)]()
+      
+      for (idx <- indices) {
+        while (sv_idx < ids.length && ids(sv_idx) < idx) {
+          sv_idx += 1
+        }
+        if (sv_idx < ids.length && ids(sv_idx) == idx) {
+          elements += ((new_idx, vs(sv_idx)))
+          sv_idx += 1
+        }
+        new_idx += 1
+      }
+      
+      Vectors.sparse(indices.length, elements)
+
+    case v =>
+      throw new IllegalArgumentException("Don't support vector type " + v.getClass)
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Specialized model for equivalent vectors
+ */
+@Experimental
+class SignificantSelectorEmptyModel extends SignificantSelectorModel(Array[Int]()) {
+  
+  val empty_vector = Vectors.dense(Array[Double]())
+  
+  override def transform(vector: Vector): Vector = empty_vector
+}
+
+/**
+ * :: Experimental ::
+ * Create Significant selector.
+ */
+@Experimental
+class SignificantSelector() {
+
+  /**
+   * Returns a significant vector indices selector.
+   *
+   * @param sources an `RDD[Vector]` containing the vectors.
+   */
+  def fit(sources: RDD[Vector]): SignificantSelectorModel = {
+    val sources_count = sources.count()
+    val significant_indices = sources.flatMap {
+        case DenseVector(vs) =>
+          vs.zipWithIndex
+        case SparseVector(_, ids, vs) =>
+          vs.zip(ids)
+        case v =>
+          throw new IllegalArgumentException("Don't support vector type " + v.getClass)
+      }
+      .map(e => (e.swap, 1))
+      .reduceByKey(_ + _)
+      .map { case ((idx, value), count) => (idx, (value, count))}
+      .groupByKey()
+      .mapValues { e =>
+        val values = e.groupBy(_._1)
+        val sum = e.map(_._2).sum
+
+        values.size + (if (sum == sources_count || values.contains(0.0)) 0 else 1)
+      }
+      .filter(_._2 > 1)
+      .keys
+      .collect()
+      .sorted
+
+    if (significant_indices.nonEmpty)
+      new SignificantSelectorModel(significant_indices)
+    else
+      new SignificantSelectorEmptyModel()
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/SignificantSelectorTest.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/SignificantSelectorTest.scala
new file mode 100644
index 000000000000..69ae7d9c33eb
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/SignificantSelectorTest.scala
@@ -0,0 +1,77 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.feature
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.scalatest.FunSuite
+
+class SignificantSelectorTest extends FunSuite with MLlibTestSparkContext {
+  val dv = Vectors.dense(1, 2, 3, 4, 5)
+  val sv1 = Vectors.sparse(5, Seq((0, 1.0), (1, 2.0), (2, 3.0), (3, 4.0), (4, 5.0)))
+  val sv2 = Vectors.sparse(5, Seq((2, 3.0)))
+
+  test("same result vector") {
+    val vectors = sc.parallelize(List(
+      Vectors.dense(0.0, 1.0, 2.0, 3.0, 4.0),
+      Vectors.dense(4.0, 5.0, 6.0, 7.0, 8.0)
+    ))
+
+    val significant = new SignificantSelector().fit(vectors)
+    assert(significant.transform(dv) == dv)
+    assert(significant.transform(sv1) == sv1)
+    assert(significant.transform(sv2) == sv2)
+  }
+  
+  
+  test("shortest result vector") {
+    val vectors = sc.parallelize(List(
+      Vectors.dense(0.0, 2.0, 3.0, 4.0),
+      Vectors.dense(0.0, 2.0, 3.0, 4.0),
+      Vectors.dense(0.0, 2.0, 3.0, 4.0),
+      Vectors.sparse(4, Seq((1, 3.0), (2, 4.0))),
+      Vectors.dense(0.0, 3.0, 5.0, 4.0),
+      Vectors.dense(0.0, 3.0, 7.0, 4.0)
+    ))
+
+    val significant = new SignificantSelector().fit(vectors)
+
+    val significanted_dv = Vectors.dense(2.0, 3.0, 4.0)
+    val significanted_sv1 = Vectors.sparse(3, Seq((0, 2.0), (1, 3.0), (2, 4.0)))
+    val significanted_sv2 = Vectors.sparse(3, Seq((1, 3.0)))
+
+    assert(significant.transform(dv) == significanted_dv)
+    assert(significant.transform(sv1) == significanted_sv1)
+    assert(significant.transform(sv2) == significanted_sv2)
+  }
+  
+  test("empty result vector") {
+    val vectors = sc.parallelize(List(
+      Vectors.dense(0.0, 2.0, 3.0, 4.0),
+      Vectors.dense(0.0, 2.0, 3.0, 4.0)
+    ))
+
+    val significant = new SignificantSelector().fit(vectors)
+
+    val empty_vector = Vectors.dense(Array[Double]())
+
+    assert(significant.transform(dv) == empty_vector)
+    assert(significant.transform(sv1) == empty_vector)
+    assert(significant.transform(sv2) == empty_vector)
+  }
+}
\ No newline at end of file