Clustering with a Domain-Specific Distance Measure
Abstract
With a point matching distance measure which is invariant under translation, rotation and permutation, we learn 2-D point-set ob(cid:173) jects, by clustering noisy point-set images. Unlike traditional clus(cid:173) tering methods which use distance measures that operate on feature vectors - a representation common to most problem domains - this object-based clustering technique employs a distance measure spe(cid:173) cific to a type of object within a problem domain. Formulating the clustering problem as two nested objective functions, we derive optimization dynamics similar to the Expectation-Maximization algorithm used in mixture models.
Cite
Text
Gold et al. "Clustering with a Domain-Specific Distance Measure." Neural Information Processing Systems, 1993.Markdown
[Gold et al. "Clustering with a Domain-Specific Distance Measure." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/gold1993neurips-clustering/)BibTeX
@inproceedings{gold1993neurips-clustering,
title = {{Clustering with a Domain-Specific Distance Measure}},
author = {Gold, Steven and Mjolsness, Eric and Rangarajan, Anand},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {96-103},
url = {https://mlanthology.org/neurips/1993/gold1993neurips-clustering/}
}