diff --git a/src/mlpack/core/tree/space_split/projection_vector.hpp b/src/mlpack/core/tree/space_split/projection_vector.hpp
index c32e840964..b086e9e511 100644
--- a/src/mlpack/core/tree/space_split/projection_vector.hpp
+++ b/src/mlpack/core/tree/space_split/projection_vector.hpp
@@ -107,7 +107,7 @@ class ProjVector
    * @param vect Vector to be considered.
    */
   ProjVector(const arma::vec& vect) :
-      projVect(arma::normalise(vect))
+      projVect(normalise(vect))
   {};
 
   /**
diff --git a/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp
index d7697256a6..044ae38009 100644
--- a/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp
+++ b/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp
@@ -91,8 +91,8 @@ void CosineEmbeddingLossType<MatType>::Backward(
     {
       const int multiplier = similarity ? 1 : -1;
       outputTemp(arma::span(i, i + cols -1)) = -1 * multiplier *
-          (arma::normalise(inputTemp2(arma::span(i, i + cols - 1))) -
-          cosDist * arma::normalise(inputTemp1(arma::span(i, i + cols -
+          (normalise(inputTemp2(arma::span(i, i + cols - 1))) -
+          cosDist * normalise(inputTemp1(arma::span(i, i + cols -
           1)))) / std::sqrt(arma::accu(arma::pow(inputTemp1(arma::span(i, i +
           cols - 1)), 2)));
     }
diff --git a/src/mlpack/methods/cf/neighbor_search_policies/cosine_search.hpp b/src/mlpack/methods/cf/neighbor_search_policies/cosine_search.hpp
index 5b07168ec3..9c33509e79 100644
--- a/src/mlpack/methods/cf/neighbor_search_policies/cosine_search.hpp
+++ b/src/mlpack/methods/cf/neighbor_search_policies/cosine_search.hpp
@@ -52,7 +52,7 @@ class CosineSearch
   CosineSearch(const arma::mat& referenceSet)
   {
     // Normalize all vectors to unit length.
-    arma::mat normalizedSet = arma::normalise(referenceSet, 2, 0);
+    arma::mat normalizedSet = normalise(referenceSet, 2, 0);
 
     neighborSearch.Train(std::move(normalizedSet));
   }
@@ -70,7 +70,7 @@ class CosineSearch
               arma::Mat<size_t>& neighbors, arma::mat& similarities)
   {
     // Normalize query vectors to unit length.
-    arma::mat normalizedQuery = arma::normalise(query, 2, 0);
+    arma::mat normalizedQuery = normalise(query, 2, 0);
 
     neighborSearch.Search(normalizedQuery, k, neighbors, similarities);
 
diff --git a/src/mlpack/methods/cf/neighbor_search_policies/pearson_search.hpp b/src/mlpack/methods/cf/neighbor_search_policies/pearson_search.hpp
index 8602a0f55f..24e7fce84b 100644
--- a/src/mlpack/methods/cf/neighbor_search_policies/pearson_search.hpp
+++ b/src/mlpack/methods/cf/neighbor_search_policies/pearson_search.hpp
@@ -57,7 +57,7 @@ class PearsonSearch
     // For each vector x, first subtract mean(x) from each element in x.
     // Then normalize the vector to unit length.
     arma::mat normalizedSet(arma::size(referenceSet));
-    normalizedSet = arma::normalise(
+    normalizedSet = normalise(
         referenceSet.each_row() - arma::mean(referenceSet));
 
     neighborSearch.Train(std::move(normalizedSet));
@@ -79,7 +79,7 @@ class PearsonSearch
     // For each vector x, first subtract mean(x) from each element in x.
     // Then normalize the vector to unit length.
     arma::mat normalizedQuery;
-    normalizedQuery = arma::normalise(query.each_row() - arma::mean(query));
+    normalizedQuery = normalise(query.each_row() - arma::mean(query));
 
     neighborSearch.Search(normalizedQuery, k, neighbors, similarities);
 
diff --git a/src/mlpack/tests/ann/recurrent_network_test.cpp b/src/mlpack/tests/ann/recurrent_network_test.cpp
index df33f7e1ff..75765a4457 100644
--- a/src/mlpack/tests/ann/recurrent_network_test.cpp
+++ b/src/mlpack/tests/ann/recurrent_network_test.cpp
@@ -262,7 +262,7 @@ void GenerateNoisySinRNN(arma::cube& data,
 
   arma::colvec y = x;
   if (normalize)
-    y = arma::normalise(x);
+    y = normalise(x);
 
   // Now break this into columns of rho size slices.
   size_t numColumns = y.n_elem / rho;