diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index be4ce5e891f27..2fa01fbc9d9dd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -124,7 +124,7 @@ class KMeans private (
     }
 
     // Compute squared norms and cache them.
-    val norms = data.map(_.norm(2.0))
+    val norms = data.map(Vectors.norm(_, 2.0))
     norms.persist()
     val breezeData = data.map(_.toBreeze).zip(norms).map { case (v, norm) =>
       new BreezeVectorWithNorm(v, norm)
@@ -424,7 +424,7 @@ object KMeans {
 private[clustering]
 class BreezeVectorWithNorm(val vector: BV[Double], val norm: Double) extends Serializable {
 
-  def this(vector: BV[Double]) = this(vector, Vectors.fromBreeze(vector).norm(2.0))
+  def this(vector: BV[Double]) = this(vector, Vectors.norm(Vectors.fromBreeze(vector), 2.0))
 
   def this(array: Array[Double]) = this(new BDV[Double](array))
 
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 5d36337f5887e..1ced26a9b70a2 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
@@ -45,7 +45,7 @@ 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 = {
-    val norm = vector.norm(p)
+    val norm = Vectors.norm(vector, p)
 
     if (norm != 0.0) {
       // For dense vector, we've to allocate new memory for new output vector.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 81f11cf147a74..47d1a76fa361d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -85,13 +85,6 @@ sealed trait Vector extends Serializable {
    *          with type `Double`.
    */
   private[spark] def foreachActive(f: (Int, Double) => Unit)
-
-  /**
-   * Returns the p-norm of this vector.
-   * @param p norm.
-   * @return norm in L^p^ space.
-   */
-  private[spark] def norm(p: Double): Double
 }
 
 /**
@@ -269,9 +262,21 @@ object Vectors {
     }
   }
 
-  private[linalg] def norm(p: Double, values: Array[Double]): Double = {
+  /**
+   * Returns the p-norm of this vector.
+   * @param vector input vector.
+   * @param p norm.
+   * @return norm in L^p^ space.
+   */
+  private[spark] def norm(vector: Vector, p: Double): Double = {
     require(p >= 1.0)
+    val values = vector match {
+      case dv: DenseVector => dv.values
+      case sv: SparseVector => sv.values
+      case v => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
+    }
     val size = values.size
+
     if (p == 1) {
       var sum = 0.0
       var i = 0
@@ -339,8 +344,6 @@ class DenseVector(val values: Array[Double]) extends Vector {
       i += 1
     }
   }
-
-  private[spark] override def norm(p: Double): Double = Vectors.norm(p, values)
 }
 
 /**
@@ -389,6 +392,4 @@ class SparseVector(
       i += 1
     }
   }
-
-  private[spark] override def norm(p: Double): Double = Vectors.norm(p, values)
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
index 1181e8ffc73a4..f04ce6b6d8e8f 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
@@ -203,18 +203,22 @@ class VectorsSuite extends FunSuite {
     val dv = Vectors.dense(0.0, -1.2, 3.1, 0.0, -4.5, 1.9)
     val sv = Vectors.sparse(6, Seq((1, -1.2), (2, 3.1), (3, 0.0), (4, -4.5), (5, 1.9)))
 
-    assert(dv.norm(1.0) ~== dv.toArray.foldLeft(0.0)((a, v) => a + math.abs(v)) relTol 1E-8)
-    assert(sv.norm(1.0) ~== sv.toArray.foldLeft(0.0)((a, v) => a + math.abs(v)) relTol 1E-8)
+    assert(Vectors.norm(dv, 1.0) ~== dv.toArray.foldLeft(0.0)((a, v) =>
+      a + math.abs(v)) relTol 1E-8)
+    assert(Vectors.norm(sv, 1.0) ~== sv.toArray.foldLeft(0.0)((a, v) =>
+      a + math.abs(v)) relTol 1E-8)
 
-    assert(dv.norm(2.0) ~== math.sqrt(dv.toArray.foldLeft(0.0)((a, v) => a + v * v)) relTol 1E-8)
-    assert(sv.norm(2.0) ~== math.sqrt(sv.toArray.foldLeft(0.0)((a, v) => a + v * v)) relTol 1E-8)
+    assert(Vectors.norm(dv, 2.0) ~== math.sqrt(dv.toArray.foldLeft(0.0)((a, v) =>
+      a + v * v)) relTol 1E-8)
+    assert(Vectors.norm(sv, 2.0) ~== math.sqrt(sv.toArray.foldLeft(0.0)((a, v) =>
+      a + v * v)) relTol 1E-8)
 
-    assert(dv.norm(Double.PositiveInfinity) ~== dv.toArray.map(math.abs).max relTol 1E-8)
-    assert(sv.norm(Double.PositiveInfinity) ~== sv.toArray.map(math.abs).max relTol 1E-8)
+    assert(Vectors.norm(dv, Double.PositiveInfinity) ~== dv.toArray.map(math.abs).max relTol 1E-8)
+    assert(Vectors.norm(sv, Double.PositiveInfinity) ~== sv.toArray.map(math.abs).max relTol 1E-8)
 
-    assert(dv.norm(3.7) ~== math.pow(dv.toArray.foldLeft(0.0)((a, v) =>
+    assert(Vectors.norm(dv, 3.7) ~== math.pow(dv.toArray.foldLeft(0.0)((a, v) =>
       a + math.pow(math.abs(v), 3.7)), 1.0 / 3.7) relTol 1E-8)
-    assert(sv.norm(3.7) ~== math.pow(dv.toArray.foldLeft(0.0)((a, v) =>
+    assert(Vectors.norm(sv, 3.7) ~== math.pow(dv.toArray.foldLeft(0.0)((a, v) =>
       a + math.pow(math.abs(v), 3.7)), 1.0 / 3.7) relTol 1E-8)
   }
 }