diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 07d395e78ee12..542977a48f0ac 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -1457,9 +1457,9 @@ class LogisticRegressionSuite
     */
 
     val coefficientsR = new DenseMatrix(3, 2, Array(
-      0.1881871, -0.0,
+      0.1881871, 0.0,
       -0.02412645, 0.0,
-      -0.1640607, -0.0), isTransposed = true)
+      -0.1640607, 0.0), isTransposed = true)
     val interceptsR = Vectors.dense(0.2658824, 0.53604701, -0.8019294)
 
     model.coefficientMatrix.colIter.foreach(v => assert(v.toArray.sum ~== 0.0 absTol eps))
diff --git a/mllib/src/test/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregatorSuite.scala
index bcbcb0e8efab9..16ef4af4f94e8 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregatorSuite.scala
@@ -28,6 +28,7 @@ class LogisticAggregatorSuite extends SparkFunSuite with MLlibTestSparkContext {
 
   @transient var instances: Array[Instance] = _
   @transient var instancesConstantFeature: Array[Instance] = _
+  @transient var instancesConstantFeatureFiltered: Array[Instance] = _
 
   override def beforeAll(): Unit = {
     super.beforeAll()
@@ -41,6 +42,11 @@ class LogisticAggregatorSuite extends SparkFunSuite with MLlibTestSparkContext {
       Instance(1.0, 0.5, Vectors.dense(1.0, 1.0)),
       Instance(2.0, 0.3, Vectors.dense(1.0, 0.5))
     )
+    instancesConstantFeatureFiltered = Array(
+      Instance(0.0, 0.1, Vectors.dense(2.0)),
+      Instance(1.0, 0.5, Vectors.dense(1.0)),
+      Instance(2.0, 0.3, Vectors.dense(0.5))
+    )
   }
 
   /** Get summary statistics for some data and create a new LogisticAggregator. */
@@ -233,30 +239,44 @@ class LogisticAggregatorSuite extends SparkFunSuite with MLlibTestSparkContext {
     val binaryInstances = instancesConstantFeature.map { instance =>
       if (instance.label <= 1.0) instance else Instance(0.0, instance.weight, instance.features)
     }
+    val binaryInstancesFiltered = instancesConstantFeatureFiltered.map { instance =>
+      if (instance.label <= 1.0) instance else Instance(0.0, instance.weight, instance.features)
+    }
     val coefArray = Array(1.0, 2.0, -2.0, 3.0, 0.0, -1.0)
+    val coefArrayFiltered = Array(3.0, 0.0, -1.0)
     val interceptArray = Array(4.0, 2.0, -3.0)
     val aggConstantFeature = getNewAggregator(instancesConstantFeature,
       Vectors.dense(coefArray ++ interceptArray), fitIntercept = true, isMultinomial = true)
-    instances.foreach(aggConstantFeature.add)
+    val aggConstantFeatureFiltered = getNewAggregator(instancesConstantFeatureFiltered,
+      Vectors.dense(coefArrayFiltered ++ interceptArray), fitIntercept = true, isMultinomial = true)
+
+    instancesConstantFeature.foreach(aggConstantFeature.add)
+    instancesConstantFeatureFiltered.foreach(aggConstantFeatureFiltered.add)
 
     // constant features should not affect gradient
-    def validateGradient(grad: Vector): Unit = {
-      assert(grad(0) === 0.0)
-      grad.toArray.foreach { gradientValue =>
-        assert(!gradientValue.isNaN &&
-          gradientValue > Double.NegativeInfinity && gradientValue < Double.PositiveInfinity)
+    def validateGradient(grad: Vector, gradFiltered: Vector, numCoefficientSets: Int): Unit = {
+      for (i <- 0 until numCoefficientSets) {
+        assert(grad(i) === 0.0)
+        assert(grad(numCoefficientSets + i) == gradFiltered(i))
       }
     }
 
-    validateGradient(aggConstantFeature.gradient)
+    validateGradient(aggConstantFeature.gradient, aggConstantFeatureFiltered.gradient, 3)
 
     val binaryCoefArray = Array(1.0, 2.0)
+    val binaryCoefArrayFiltered = Array(2.0)
     val intercept = 1.0
     val aggConstantFeatureBinary = getNewAggregator(binaryInstances,
       Vectors.dense(binaryCoefArray ++ Array(intercept)), fitIntercept = true,
       isMultinomial = false)
-    instances.foreach(aggConstantFeatureBinary.add)
+    val aggConstantFeatureBinaryFiltered = getNewAggregator(binaryInstancesFiltered,
+      Vectors.dense(binaryCoefArrayFiltered ++ Array(intercept)), fitIntercept = true,
+      isMultinomial = false)
+    binaryInstances.foreach(aggConstantFeatureBinary.add)
+    binaryInstancesFiltered.foreach(aggConstantFeatureBinaryFiltered.add)
+
     // constant features should not affect gradient
-    validateGradient(aggConstantFeatureBinary.gradient)
+    validateGradient(aggConstantFeatureBinary.gradient,
+      aggConstantFeatureBinaryFiltered.gradient, 1)
   }
 }