diff --git a/mllib/src/main/scala/org/apache/spark/mllib/MLContext.scala b/mllib/src/main/scala/org/apache/spark/mllib/MLContext.scala
index eefca193ec53e..fb4d458cd8a09 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/MLContext.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/MLContext.scala
@@ -34,24 +34,37 @@ class MLContext(self: SparkContext) {
    * where the feature indices are converted to zero-based.
    *
    * @param path file or directory path in any Hadoop-supported file system URI
-   * @param numFeatures number of features
-   * @param labelParser parser for labels, default: _.toDouble
+   * @param numFeatures number of features, it will be determined from input
+   *                    if a non-positive value is given
+   *@param labelParser parser for labels, default: _.toDouble
    * @return labeled data stored as an RDD[LabeledPoint]
    */
   def libSVMFile(
       path: String,
       numFeatures: Int,
       labelParser: String => Double = _.toDouble): RDD[LabeledPoint] = {
-    self.textFile(path).map(_.trim).filter(!_.isEmpty).map { line =>
-      val items = line.split(' ')
+    val parsed = self.textFile(path).map(_.trim).filter(!_.isEmpty).map(_.split(' '))
+    // Determine number of features.
+    val d = if (numFeatures > 0) {
+      numFeatures
+    } else {
+      parsed.map { items =>
+        if (items.length > 1) {
+          items.last.split(':')(0).toInt
+        } else {
+          0
+        }
+      }.reduce(math.max)
+    }
+    parsed.map { items =>
       val label = labelParser(items.head)
-      val features = Vectors.sparse(numFeatures, items.tail.map { item =>
+      val (indices, values) = items.tail.map { item =>
         val indexAndValue = item.split(':')
         val index = indexAndValue(0).toInt - 1
         val value = indexAndValue(1).toDouble
         (index, value)
-      })
-      LabeledPoint(label, features)
+      }.unzip
+      LabeledPoint(label, Vectors.sparse(d, indices.toArray, values.toArray))
     }
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/MLContextSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/MLContextSuite.scala
index 6762f8c479e98..743102b54fa9e 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/MLContextSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/MLContextSuite.scala
@@ -33,17 +33,26 @@ class MLContextSuite extends FunSuite with LocalSparkContext {
     val lines =
       """
         |1 1:1.0 3:2.0 5:3.0
+        |0
         |0 2:4.0 4:5.0 6:6.0
       """.stripMargin
     val tempDir = Files.createTempDir()
     val file = new File(tempDir.getPath, "part-00000")
     Files.write(lines, file, Charsets.US_ASCII)
-    val points = sc.libSVMFile(tempDir.toURI.toString, 6).collect()
-    assert(points.length === 2)
-    assert(points(0).label === 1.0)
-    assert(points(0).features === Vectors.sparse(6, Seq((0, 1.0), (2, 2.0), (4, 3.0))))
-    assert(points(1).label === 0.0)
-    assert(points(1).features === Vectors.sparse(6, Seq((1, 4.0), (3, 5.0), (5, 6.0))))
+
+    val pointsWithNumFeatures = sc.libSVMFile(tempDir.toURI.toString, 6).collect()
+    val pointsWithoutNumFeatures = sc.libSVMFile(tempDir.toURI.toString, 0).collect()
+
+    for (points <- Seq(pointsWithNumFeatures, pointsWithoutNumFeatures)) {
+      assert(points.length === 3)
+      assert(points(0).label === 1.0)
+      assert(points(0).features === Vectors.sparse(6, Seq((0, 1.0), (2, 2.0), (4, 3.0))))
+      assert(points(1).label == 0.0)
+      assert(points(1).features == Vectors.sparse(6, Seq()))
+      assert(points(2).label === 0.0)
+      assert(points(2).features === Vectors.sparse(6, Seq((1, 4.0), (3, 5.0), (5, 6.0))))
+    }
+
     try {
       file.delete()
       tempDir.delete()