Steven Gold, Anand Rangarajan, Eric Mjolsness
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. invariant under affine transformations - translation, rotation, scale and shear - and permutations. 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. Learning is formulated as an optimization problem . Large objectives so formulated ('" million variables) are efficiently minimized using a combination of optimization techniques - alge(cid:173) braic transformations, iterative projective scaling, clocked objec(cid:173) tives, and deterministic annealing.