diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index b9621530efa22..3e1ed91bf6729 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -136,25 +136,28 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
     // Prepend an extra variable consisting of all 1.0's for the intercept.
     val data = if (addIntercept) {
-      input.map(labeledPoint => (labeledPoint.label, labeledPoint.features.+:(1.0)))
+      input.map(labeledPoint => (labeledPoint.label, 1.0 +: labeledPoint.features))
     } else {
       input.map(labeledPoint => (labeledPoint.label, labeledPoint.features))
     }
 
     val initialWeightsWithIntercept = if (addIntercept) {
-      initialWeights.+:(1.0)
+      0.0 +: initialWeights
     } else {
       initialWeights
     }
 
-    val weights = optimizer.optimize(data, initialWeightsWithIntercept)
-    val intercept = weights(0)
-    val weightsScaled = weights.tail
+    val weightsWithIntercept = optimizer.optimize(data, initialWeightsWithIntercept)
 
-    val model = createModel(weightsScaled, intercept)
+    val (intercept, weights) = if (addIntercept) {
+      (weightsWithIntercept(0), weightsWithIntercept.tail)
+    } else {
+      (0.0, weightsWithIntercept)
+    }
+
+    logInfo("Final weights " + weights.mkString(","))
+    logInfo("Final intercept " + intercept)
 
-    logInfo("Final model weights " + model.weights.mkString(","))
-    logInfo("Final model intercept " + model.intercept)
-    model
+    createModel(weights, intercept)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
index 281f9df36ddb3..5d251bcbf35db 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.mllib.regression
 
-import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext}
@@ -57,4 +56,29 @@ class LinearRegressionSuite extends FunSuite with LocalSparkContext {
     // Test prediction on Array.
     validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
   }
+
+  // Test if we can correctly learn Y = 10*X1 + 10*X2
+  test("linear regression without intercept") {
+    val testRDD = sc.parallelize(LinearDataGenerator.generateLinearInput(
+      0.0, Array(10.0, 10.0), 100, 42), 2).cache()
+    val linReg = new LinearRegressionWithSGD().setIntercept(false)
+    linReg.optimizer.setNumIterations(1000).setStepSize(1.0)
+
+    val model = linReg.run(testRDD)
+
+    assert(model.intercept === 0.0)
+    assert(model.weights.length === 2)
+    assert(model.weights(0) >= 9.0 && model.weights(0) <= 11.0)
+    assert(model.weights(1) >= 9.0 && model.weights(1) <= 11.0)
+
+    val validationData = LinearDataGenerator.generateLinearInput(
+      0.0, Array(10.0, 10.0), 100, 17)
+    val validationRDD = sc.parallelize(validationData, 2).cache()
+
+    // Test prediction on RDD.
+    validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)
+
+    // Test prediction on Array.
+    validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
+  }
 }