change tag

doc/source/examples/analysis/reduced_dimensions/README.rst

@@ -17,7 +17,7 @@ reduction.
 
 #. **Diffusion maps** are a non-linear dimensionality reduction technique that embeds the coordinates of each frame onto a lower-dimensional space, such that the distance between each frame in the lower-dimensional space represents their "diffusion distance", or similarity. It integrates local information about the similarity of each point to its neighours, into a global geometry of the intrinsic manifold. This means that this technique is not suitable for trajectories where the transitions between conformational states is not well-sampled (e.g. replica exchange simulations), as the regions may become disconnected and a meaningful global geometry cannot be approximated. Unlike PCA, there is no explicit mapping between the components of the lower-dimensional space and the original atomic coordinates; no physical interpretation of the eigenvectors is immediately available.
 
-For computing similarity, see the tutorials in :ref:`trajectory-similarity-analysis`.
+For computing similarity, see the tutorials in :ref:`_trajectory-similarity-overview`.
 
 .. toctree::
    :maxdepth: 1

doc/source/examples/analysis/trajectory_similarity/README.rst

@@ -1,5 +1,5 @@
 .. -*- coding: utf-8 -*-
-.. _trajectory-similarity-analysis:
+.. _trajectory-similarity-overview:
 
 ======================
 Trajectory similarity