Context-Dependent Similarity
Artificial Intelligence
2013-04-05 v1
Abstract
Attribute weighting and differential weighting, two major mechanisms for computing context-dependent similarity or dissimilarity measures are studied and compared. A dissimilarity measure based on subset size in the context is proposed and its metrization and application are given. It is also shown that while all attribute weighting dissimilarity measures are metrics differential weighting dissimilarity measures are usually non-metric.
Keywords
Cite
@article{arxiv.1304.1084,
title = {Context-Dependent Similarity},
author = {Yizong Cheng},
journal= {arXiv preprint arXiv:1304.1084},
year = {2013}
}
Comments
Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)