diff --git a/bnpm/similarity.py b/bnpm/similarity.py
index da0a8dd..0f30a01 100644
--- a/bnpm/similarity.py
+++ b/bnpm/similarity.py
@@ -234,7 +234,7 @@ def orthogonalize(v1, v2, method='OLS', device='cpu', thresh_EVR_PCA=1e-15):
     return v1_orth, EVR, EVR_total, pca_dict
 
 
-def orthogonalize_matrix_nearest(X: Union[np.ndarray, torch.Tensor]):
+def orthogonalize_matrix_nearest(X: Union[np.ndarray, torch.Tensor], center=True):
     """
     Orthogonalizes a matrix by finding the nearest orthogonal matrix. Nearest is
     defined as solving the Procrustes problem via minimizing the Frobenius norm
@@ -248,6 +248,8 @@ def orthogonalize_matrix_nearest(X: Union[np.ndarray, torch.Tensor]):
     Args:
         X (ndarray):
             Matrix to orthogonalize. shape: (n_samples, n_features)
+        center (bool):
+            Whether to center the matrix. Default is True.
 
     Returns:
         (ndarray):
@@ -258,6 +260,9 @@ def orthogonalize_matrix_nearest(X: Union[np.ndarray, torch.Tensor]):
     elif isinstance(X, torch.Tensor):
         op, qr = torch_helpers.orthogonal_procrustes, torch.linalg.qr
 
+    if center:
+        X = X - X.mean(axis=0, keepdims=True)
+
     Q = qr(X)[0]
     w, scale = op(Q, X)
     return w @ scale