Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures

Prior constraints are imposed upon a learning problem in the form of distance measures. Prototypical 2-D point sets and graphs are learned by clustering with point matching and graph matching dis(cid:173) tance measures. The point matching distance measure is approx. It operates between noisy images with missing and spurious points. The graph matching distance measure operates on weighted graphs and is invariant under per(cid:173) mutations.