diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
index 90eceb0d61b40..0f51e23323177 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
@@ -25,9 +25,8 @@ import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param.{ParamMap, Params}
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util._
-import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
 import org.apache.spark.mllib.stat.Statistics
-import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{StructField, StructType}
@@ -70,7 +69,7 @@ class MaxAbsScaler @Since("2.0.0") (@Since("2.0.0") override val uid: String)
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): MaxAbsScalerModel = {
     transformSchema(dataset.schema, logging = true)
-    val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map {
+    val input = dataset.select($(inputCol)).rdd.map {
       case Row(v: Vector) => OldVectors.fromML(v)
     }
     val summary = Statistics.colStats(input)
@@ -79,7 +78,7 @@ class MaxAbsScaler @Since("2.0.0") (@Since("2.0.0") override val uid: String)
     val n = minVals.length
     val maxAbs = Array.tabulate(n) { i => math.max(math.abs(minVals(i)), math.abs(maxVals(i))) }
 
-    copyValues(new MaxAbsScalerModel(uid, Vectors.dense(maxAbs)).setParent(this))
+    copyValues(new MaxAbsScalerModel(uid, Vectors.dense(maxAbs).compressed).setParent(this))
   }
 
   @Since("2.0.0")
@@ -121,13 +120,13 @@ class MaxAbsScalerModel private[ml] (
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
     transformSchema(dataset.schema, logging = true)
-    // TODO: this looks hack, we may have to handle sparse and dense vectors separately.
-    val maxAbsUnzero = Vectors.dense(maxAbs.toArray.map(x => if (x == 0) 1 else x))
-    val reScale = udf { (vector: Vector) =>
-      val brz = vector.asBreeze / maxAbsUnzero.asBreeze
-      Vectors.fromBreeze(brz)
-    }
-    dataset.withColumn($(outputCol), reScale(col($(inputCol))))
+
+    val scale = maxAbs.toArray.map { v => if (v == 0) 1.0 else 1 / v }
+    val func = StandardScalerModel.getTransformFunc(
+      Array.empty, scale, false, true)
+    val transformer = udf(func)
+
+    dataset.withColumn($(outputCol), transformer(col($(inputCol))))
   }
 
   @Since("2.0.0")