scikit-learn
diff --git a/‎doc/whats_new.rst
+4 b/‎doc/whats_new.rst
+4
diff --git a/‎sklearn/metrics/pairwise.py
+37-11 b/‎sklearn/metrics/pairwise.py
+37-11
@@ -91,6 +91,10 @@ Enhancements
      handle unknown categorical features more gracefully during transform.
      By `Manoj Kumar`_
 
+   - Added option ``check_X_y`` to :func:`metrics.pairwise_distances_argmin_min`
+     that can give speed improvements by avoiding repeated checking when set to
+     False. By `Manoj Kumar`_
+
 Documentation improvements
 ..........................
 
 
@@ -56,6 +56,30 @@
 
 
 # Utility Functions
+def _return_float_dtype(X, Y):
+    """
+    1. If dtype of X and Y is float32, then dtype float32 is returned.
+    2. Else dtype float is returned.
+    """
+    if not issparse(X) and not isinstance(X, np.ndarray):
+        X = np.asarray(X)
+
+    if Y is None:
+        Y_dtype = X.dtype
+    elif not issparse(Y) and not isinstance(Y, np.ndarray):
+        Y = np.asarray(Y)
+        Y_dtype = Y.dtype
+    else:
+        Y_dtype = Y.dtype
+
+    if X.dtype == Y_dtype == np.float32:
+        dtype = np.float32
+    else:
+        dtype = np.float
+
+    return X, Y, dtype
+
+
 def check_pairwise_arrays(X, Y):
     """ Set X and Y appropriately and checks inputs
 
@@ -85,22 +109,18 @@ def check_pairwise_arrays(X, Y):
         If Y was None, safe_Y will be a pointer to X.
 
     """
+    X, Y, dtype = _return_float_dtype(X, Y)
+
     if Y is X or Y is None:
-        X = Y = check_array(X, accept_sparse='csr')
+        X = Y = check_array(X, accept_sparse='csr', dtype=dtype)
     else:
-        X = check_array(X, accept_sparse='csr')
-        Y = check_array(Y, accept_sparse='csr')
+        X = check_array(X, accept_sparse='csr', dtype=dtype)
+        Y = check_array(Y, accept_sparse='csr', dtype=dtype)
     if X.shape[1] != Y.shape[1]:
         raise ValueError("Incompatible dimension for X and Y matrices: "
                          "X.shape[1] == %d while Y.shape[1] == %d" % (
                              X.shape[1], Y.shape[1]))
 
-    if not (X.dtype == Y.dtype == np.float32):
-        if Y is X:
-            X = Y = check_array(X, ['csr', 'csc', 'coo'], dtype=np.float)
-        else:
-            X = check_array(X, ['csr', 'csc', 'coo'], dtype=np.float)
-            Y = check_array(Y, ['csr', 'csc', 'coo'], dtype=np.float)
     return X, Y
 
 
@@ -225,7 +245,8 @@ def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False):
 
 
 def pairwise_distances_argmin_min(X, Y, axis=1, metric="euclidean",
-                                  batch_size=500, metric_kwargs=None):
+                                  batch_size=500, metric_kwargs=None,
+                                  check_X_y=True):
     """Compute minimum distances between one point and a set of points.
 
     This function computes for each row in X, the index of the row of Y which
@@ -280,6 +301,10 @@ def pairwise_distances_argmin_min(X, Y, axis=1, metric="euclidean",
     metric_kwargs : dict, optional
         Keyword arguments to pass to specified metric function.
 
+    check_X_y : bool, default True
+        Whether or not to check X and y for shape, validity and dtype. Speed
+        improvements possible if set to False when called repeatedly.
+
     Returns
     -------
     argmin : numpy.ndarray
@@ -300,7 +325,8 @@ def pairwise_distances_argmin_min(X, Y, axis=1, metric="euclidean",
     elif not callable(metric) and not isinstance(metric, str):
         raise ValueError("'metric' must be a string or a callable")
 
-    X, Y = check_pairwise_arrays(X, Y)
+    if check_X_y:
+        X, Y = check_pairwise_arrays(X, Y)
 
     if metric_kwargs is None:
         metric_kwargs = {}