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 c46c6f886e7c9..f5cf732506181 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
@@ -725,7 +725,6 @@ class SparseVector(
       -1
     } else {
 
-      //grab first active index and value by default
       var maxIdx = indices(0)
       var maxValue = values(0)
 
@@ -736,9 +735,14 @@ class SparseVector(
         }
       }
 
-      // look for inactive values incase all active node values are negative
+      // look for inactive values in case all active node values are negative
       if(size != values.size && maxValue <= 0){
-        maxIdx = calcInactiveIdx(0)
+        val firstInactiveIdx = calcFirstInactiveIdx(0)
+        if(maxValue == 0){
+          if(firstInactiveIdx >= maxIdx) maxIdx else maxIdx = firstInactiveIdx
+        }else{
+          maxIdx = firstInactiveIdx
+        }
         maxValue = 0
       }
       maxIdx
@@ -751,12 +755,12 @@ class SparseVector(
    * @param idx starting index of computation
    * @return index of first inactive node
    */
-  private[SparseVector] def calcInactiveIdx(idx: Int): Int = {
+  private[SparseVector] def calcFirstInactiveIdx(idx: Int): Int = {
     if (idx < size) {
       if (!indices.contains(idx)) {
         idx
       } else {
-        calcInactiveIdx(idx + 1)
+        calcFirstInactiveIdx(idx + 1)
       }
     } else {
       -1
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 118855baecae0..d4dd7f2e0d3d6 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
@@ -95,19 +95,24 @@ class VectorsSuite extends FunSuite {
     val max2 = vec3.argmax
     assert(max2 === 2)
 
-    // check for case that sparse vector is created with only negative vaues {0.0, 0.0,-1.0, -0.7, 0.0}
-    val vec4 = Vectors.sparse(5,Array(2, 3),Array(-1.0,-.7))
+    // check for case that sparse vector is created with only negative values {0.0, 0.0,-1.0, -0.7, 0.0}
+    val vec4 = Vectors.sparse(5,Array(0, 1, 2, 3),Array(0.0, 0.0, -1.0,-.7))
     val max3 = vec4.argmax
     assert(max3 === 0)
 
-    // check for case that sparse vector is created with only negative vaues {-1.0, 0.0, -0.7, 0.0, 0.0}
-    val vec5 = Vectors.sparse(5,Array(0, 3),Array(-1.0,-.7))
+    val vec5 = Vectors.sparse(11,Array(0, 3, 10),Array(-1.0,-.7,0.0))
     val max4 = vec5.argmax
     assert(max4 === 1)
 
-    val vec6 = Vectors.sparse(2,Array(0, 1),Array(-1.0, 0.0))
+    val vec6 = Vectors.sparse(5,Array(0, 1, 3),Array(-1.0, 0.0, -.7))
     val max5 = vec6.argmax
     assert(max5 === 1)
+
+    // test that converting the sparse vector to another sparse vector then calling argmax still works right
+    var vec8 = Vectors.sparse(5,Array(0, 1),Array(0.0, -1.0))
+    vec8 = vec8.toSparse
+    val max7 = vec8.argmax
+    assert(max7 === 0)
   }
 
   test("vector equals") {