diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
index dfad25d57c..a9c2e23717 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.mllib.feature
 
-import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, norm => brzNorm}
+import breeze.linalg.{norm => brzNorm}
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 
 /**
  * :: Experimental ::
@@ -47,22 +47,31 @@ class Normalizer(p: Double) extends VectorTransformer {
    * @return normalized vector. If the norm of the input is zero, it will return the input vector.
    */
   override def transform(vector: Vector): Vector = {
-    var norm = brzNorm(vector.toBreeze, p)
+    val norm = brzNorm(vector.toBreeze, p)
 
     if (norm != 0.0) {
       // For dense vector, we've to allocate new memory for new output vector.
       // However, for sparse vector, the `index` array will not be changed,
       // so we can re-use it to save memory.
-      vector.toBreeze match {
-        case dv: BDV[Double] => Vectors.fromBreeze(dv :/ norm)
-        case sv: BSV[Double] =>
-          val output = new BSV[Double](sv.index, sv.data.clone(), sv.length)
+      vector match {
+        case dv: DenseVector =>
+          val values = dv.values.clone()
+          val size = values.size
           var i = 0
-          while (i < output.data.length) {
-            output.data(i) /= norm
+          while (i < size) {
+            values(i) /= norm
             i += 1
           }
-          Vectors.fromBreeze(output)
+          Vectors.dense(values)
+        case sv: SparseVector =>
+          val values = sv.values.clone()
+          val nnz = values.size
+          var i = 0
+          while (i < nnz) {
+            values(i) /= norm
+            i += 1
+          }
+          Vectors.sparse(sv.size, sv.indices, values)
         case v => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
       }
     } else {