Semi-supervised Graph Learning: Near Strangers or Distant Relatives

In this paper, an easily implemented semi-supervised graph learning method is presented for dimensionality reduction and clustering, using the most of prior knowledge from limited pairwise constraints. We extend instance-level constraints to space-level constraints to construct a more meaningful graph. By decomposing the (normalized) Laplacian matrix of this graph, to use the bottom eigenvectors leads to new representations of the data, which are hoped to capture the intrinsic structure. The proposed method improves the previous constrained learning methods. Furthermore, to achieve a given clustering accuracy, fewer constraints are required in our method. Experimental results demonstrate the advantages of the proposed method.