Combining Dimensions and Features in Similarity-Based Representations
Abstract
This paper develops a new representational model of similarity data that combines continuous dimensions with discrete features. An al- gorithm capable of learning these representations is described, and a Bayesian model selection approach for choosing the appropriate number of dimensions and features is developed. The approach is demonstrated on a classic data set that considers the similarities between the numbers 0 through 9.
Cite
Text
Navarro and Lee. "Combining Dimensions and Features in Similarity-Based Representations." Neural Information Processing Systems, 2002.Markdown
[Navarro and Lee. "Combining Dimensions and Features in Similarity-Based Representations." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/navarro2002neurips-combining/)BibTeX
@inproceedings{navarro2002neurips-combining,
title = {{Combining Dimensions and Features in Similarity-Based Representations}},
author = {Navarro, Daniel J. and Lee, Michael D.},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {67-74},
url = {https://mlanthology.org/neurips/2002/navarro2002neurips-combining/}
}