diff --git a/GPy/inference/latent_function_inference/inference_X.py b/GPy/inference/latent_function_inference/inference_X.py
index de3c475c..ff18680b 100644
--- a/GPy/inference/latent_function_inference/inference_X.py
+++ b/GPy/inference/latent_function_inference/inference_X.py
@@ -14,7 +14,7 @@ def inference_newX(model, Y_new, optimize=True):
 
 class Inference_X(Model):
     """The class for inference of new X with given new Y. (do_test_latent)"""
-    def __init__(self, model, Y, name='inference_X'):
+    def __init__(self, model, Y, name='inference_X', init='L2'):
         """TODO: give comments"""
         if np.isnan(Y).any():
             assert Y.shape[0]==1, "The current implementation of inference X only support one data point at a time with missing data!"
@@ -30,22 +30,25 @@ class Inference_X(Model):
         self.variational_prior = model.variational_prior.copy()
         self.Z = model.Z.copy()
         self.Y = Y
-        self.X = self._init_X(model, Y)
+        self.X = self._init_X(model, Y, init=init)
         self.compute_dL()
         
         self.link_parameter(self.X)
     
-    def _init_X(self, model, Y_new):
+    def _init_X(self, model, Y_new, init='L2'):
         # Initialize the new X by finding the nearest point in Y space.
         
         Y = model.Y
         if self.missing_data:
             Y = Y[:,self.valid_dim]
             Y_new = Y_new[:,self.valid_dim]
-            l2 = -2.*Y_new.dot(Y.T) + np.square(Y_new).sum(axis=1)[:,None]+ np.square(Y).sum(axis=1)[None,:]
+            dist = -2.*Y_new.dot(Y.T) + np.square(Y_new).sum(axis=1)[:,None]+ np.square(Y).sum(axis=1)[None,:]
         else:
-            l2 = -2.*Y_new.dot(Y.T) + np.square(Y_new).sum(axis=1)[:,None]+ np.square(Y).sum(axis=1)[None,:]
-        idx = l2.argmin(axis=1)
+            if init=='L2':
+                dist = -2.*Y_new.dot(Y.T) + np.square(Y_new).sum(axis=1)[:,None]+ np.square(Y).sum(axis=1)[None,:]
+            elif init=='NCC':
+                dist = Y_new.dot(Y.T)
+        idx = dist.argmin(axis=1)
         
         from ...models import SSGPLVM
         from ...util.misc import param_to_array