Name
Title | ||
|---|---|---|
Learning with preknowledge: Clustering with point and graph matching distance measures |
Abstract | ||
|---|---|---|
Prior knowledge constraints are imposed upon a learning problem in the form of distance measures. Prototypical 2D point sets and graphs are learned by clustering with point-matching and graph-matching distance measures. The point-matching distance measure is approximately 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 permutations. Learning is formulated as an optimization problem. Large objectives so formulated (∼ million variables) are efficiently minimized using a combination of optimization techniques---softassign, algebraic transformations, clocked objectives, and deterministic annealing. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1162/neco.1996.8.4.787 | Neural Computation |
Keywords | DocType | Volume |
optimization technique,algebraic transformation,graph-matching distance measure,clocked objective,optimization problem,large objective,deterministic annealing,affine transformation,distance measure,point-matching distance measure,graph matching | Conference | 8 |
Issue | ISSN | Citations |
4 | 0899-7667 | 43 |
PageRank | References | Authors |
4.59 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven Gold | 1 | 864 | 83.13 |
| A Rangarajan | 2 | 3698 | 367.52 |
| Eric Mjolsness | 3 | 1058 | 140.00 |