diff --git a/src/mlpack/methods/lmnn/constraints_impl.hpp b/src/mlpack/methods/lmnn/constraints_impl.hpp
index 5eaec385ae..faff89a445 100644
--- a/src/mlpack/methods/lmnn/constraints_impl.hpp
+++ b/src/mlpack/methods/lmnn/constraints_impl.hpp
@@ -238,6 +238,8 @@ void Constraints::Impostors(arma::Mat<size_t>& outputNeighbors,
   arma::Mat<size_t> neighbors;
   arma::mat distances;
 
+  outputDistance = arma::mat(k, dataset.n_cols, arma::fill::zeros);
+
   // Vectors to store indices.
   arma::uvec subIndexSame;
 
diff --git a/src/mlpack/methods/lmnn/lmnn_function_impl.hpp b/src/mlpack/methods/lmnn/lmnn_function_impl.hpp
index 171cda43c9..390ddbcd2f 100644
--- a/src/mlpack/methods/lmnn/lmnn_function_impl.hpp
+++ b/src/mlpack/methods/lmnn/lmnn_function_impl.hpp
@@ -76,8 +76,8 @@ double LMNNFunction<MetricType>::Evaluate(const arma::mat& coordinates)
   // Apply metric over dataset.
   transformedDataset = coordinates * dataset;
 
-  arma::mat distance = arma::mat(k, dataset.n_cols, arma::fill::zeros);
-  if (iteration++ % range == 0)
+  arma::mat distance;
+  if (iteration % range == 0)
   {
     // Re-calculate impostors on transformed dataset.
     Constraints constraint(transformedDataset, labels, k);
@@ -100,17 +100,22 @@ double LMNNFunction<MetricType>::Evaluate(const arma::mat& coordinates)
       // breaking point.
       for (size_t l = 0, bp = k; l < bp ; l++)
       {
-        // Check if we already have distance between impostor & data point
-        // stored.
-        double distImp = distance(l, i) > 0 ? distance(l, i) :
-            metric.Evaluate(transformedDataset.col(i),
-            transformedDataset.col(impostors(l, i)));
-
         // Calculate cost due to {data point, target neighbors, impostors}
         // triplets.
-        double eval = metric.Evaluate(transformedDataset.col(i),
-                          transformedDataset.col(targetNeighbors(j, i))) -
-                      distImp;
+        double eval = 0;
+        if (iteration++ % range == 0)
+        {
+          eval = metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(targetNeighbors(j, i))) -
+                 std::pow(distance(l, i), 2);
+        }
+        else
+        {
+          eval = metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(targetNeighbors(j, i))) -
+                 metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(impostors(l, i)));
+        }
 
         // Check bounding condition.
         if (eval < -1)
@@ -139,8 +144,8 @@ double LMNNFunction<MetricType>::Evaluate(const arma::mat& coordinates,
   // Apply metric over dataset.
   transformedDataset = coordinates * dataset;
 
-  arma::mat distance = arma::mat(k, dataset.n_cols, arma::fill::zeros);
-  if (iteration++ % range == 0)
+  arma::mat distance;
+  if (iteration % range == 0)
   {
     // Re-calculate impostors on transformed dataset.
     Constraints constraint(transformedDataset, labels, k);
@@ -162,17 +167,22 @@ double LMNNFunction<MetricType>::Evaluate(const arma::mat& coordinates,
       // Bound constraints to avoid uneccesary computation.
       for (size_t l = 0, bp = k; l < bp ; l++)
       {
-        // Check if we already have distance between impostor & data point
-        // stored.
-        double distImp = distance(l, i) > 0 ? distance(l, i) :
-            metric.Evaluate(transformedDataset.col(i),
-            transformedDataset.col(impostors(l, i)));
-
         // Calculate cost due to {data point, target neighbors, impostors}
         // triplets.
-        double eval = metric.Evaluate(transformedDataset.col(i),
-                          transformedDataset.col(targetNeighbors(j, i))) -
-                      distImp;
+        double eval = 0;
+        if (iteration++ % range == 0)
+        {
+          eval = metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(targetNeighbors(j, i))) -
+                 std::pow(distance(l, i), 2);
+        }
+        else
+        {
+          eval = metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(targetNeighbors(j, i))) -
+                 metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(impostors(l, i)));
+        }
 
         // Check bounding condition.
         if (eval < -1)
@@ -307,8 +317,8 @@ double LMNNFunction<MetricType>::EvaluateWithGradient(
   // Apply metric over dataset.
   transformedDataset = coordinates * dataset;
 
-  arma::mat distance = arma::mat(k, dataset.n_cols, arma::fill::zeros);
-  if (iteration++ % range == 0)
+  arma::mat distance;
+  if (iteration % range == 0)
   {
     // Re-calculate impostors on transformed dataset.
     Constraints constraint(transformedDataset, labels, k);
@@ -339,17 +349,22 @@ double LMNNFunction<MetricType>::EvaluateWithGradient(
       // Bound constraints to avoid uneccesary computation.
       for (size_t l = 0, bp = k; l < bp ; l++)
       {
-        // Check if we already have distance between impostor & data point
-        // stored.
-        double distImp = distance(l, i) > 0 ? distance(l, i) :
-            metric.Evaluate(transformedDataset.col(i),
-            transformedDataset.col(impostors(l, i)));
-
         // Calculate cost due to {data point, target neighbors, impostors}
         // triplets.
-        double eval = metric.Evaluate(transformedDataset.col(i),
-                          transformedDataset.col(targetNeighbors(j, i))) -
-                      distImp;
+        double eval = 0;
+        if (iteration++ % range == 0)
+        {
+          eval = metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(targetNeighbors(j, i))) -
+                 std::pow(distance(l, i), 2);
+        }
+        else
+        {
+          eval = metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(targetNeighbors(j, i))) -
+                 metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(impostors(l, i)));
+        }
 
         // Check bounding condition.
         if (eval < -1)
@@ -391,8 +406,8 @@ double LMNNFunction<MetricType>::EvaluateWithGradient(
   // Apply metric over dataset.
   transformedDataset = coordinates * dataset;
 
-  arma::mat distance = arma::mat(k, dataset.n_cols, arma::fill::zeros);
-  if (iteration++ % range == 0)
+  arma::mat distance;
+  if (iteration % range == 0)
   {
     // Re-calculate impostors on transformed dataset.
     Constraints constraint(transformedDataset, labels, k);
@@ -423,17 +438,22 @@ double LMNNFunction<MetricType>::EvaluateWithGradient(
       // Bound constraints to avoid uneccesary computation.
       for (size_t l = 0, bp = k; l < bp ; l++)
       {
-        // Check if we already have distance between impostor & data point
-        // stored.
-        double distImp = distance(l, i) > 0 ? distance(l, i) :
-            metric.Evaluate(transformedDataset.col(i),
-            transformedDataset.col(impostors(l, i)));
-
         // Calculate cost due to {data point, target neighbors, impostors}
         // triplets.
-        double eval = metric.Evaluate(transformedDataset.col(i),
-                          transformedDataset.col(targetNeighbors(j, i))) -
-                      distImp;
+        double eval = 0;
+        if (iteration++ % range == 0)
+        {
+          eval = metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(targetNeighbors(j, i))) -
+                 std::pow(distance(l, i), 2);
+        }
+        else
+        {
+          eval = metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(targetNeighbors(j, i))) -
+                 metric.Evaluate(transformedDataset.col(i),
+                    transformedDataset.col(impostors(l, i)));
+        }
 
         // Check bounding condition.
         if (eval < -1)