Visualizing Similarity Data with a Mixture of Maps
Abstract
We show how to visualize a set of pairwise similarities between objects by using several different two-dimensional maps, each of which captures different aspects of the similarity structure. When the objects are ambiguous words, for example, different senses of a word occur in different maps, so “river” and “loan” can both be close to “bank” without being at all close to each other. Aspect maps resemble clustering because they model pair-wise similarities as a mixture of different types of similarity, but they also resemble local multi-dimensional scaling because they model each type of similarity by a two-dimensional map. We demonstrate our method on a toy example, a database of human word association data, a large set of images of handwritten digits, and a set of feature vectors that represent words.
Cite
Text
Cook et al. "Visualizing Similarity Data with a Mixture of Maps." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Cook et al. "Visualizing Similarity Data with a Mixture of Maps." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/cook2007aistats-visualizing/)BibTeX
@inproceedings{cook2007aistats-visualizing,
title = {{Visualizing Similarity Data with a Mixture of Maps}},
author = {Cook, James and Sutskever, Ilya and Mnih, Andriy and Hinton, Geoffrey},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {67-74},
volume = {2},
url = {https://mlanthology.org/aistats/2007/cook2007aistats-visualizing/}
}