title: Cluster unseen points using Spectral Clustering
body: <p>I am using <a href="http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm" rel="nofollow">Spectral Clustering</a> method to cluster my data. The implementation seems to work properly. However, I have one problem - I have a set of unseen points (not present in the training set) and would like to cluster these based on the centroids derived by k-means (Step 5 in the paper). However, the k-means is computed on the k eigenvectors and therefore the centroids are low-dimensional.</p>
  
  <p>Does any-one knows a method that can be used to map an unseen point to a low-dimension and compute the distance between the projected point and the centroids derived by k-means in step 5.</p>