Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures
Abstract
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.
Cite
Text
Gold et al. "Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures." Neural Information Processing Systems, 1994.Markdown
[Gold et al. "Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/gold1994neurips-learning/)BibTeX
@inproceedings{gold1994neurips-learning,
title = {{Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures}},
author = {Gold, Steven and Rangarajan, Anand and Mjolsness, Eric},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {713-720},
url = {https://mlanthology.org/neurips/1994/gold1994neurips-learning/}
}