Information geometry, the embedding principle, and document classification (2005)
| Venue: | in Proceedings of the 2nd International Symposium on Information Geometry and its Applications |
| Citations: | 5 - 0 self |
BibTeX
@INPROCEEDINGS{Lebanon05informationgeometry,,
author = {Guy Lebanon},
title = {Information geometry, the embedding principle, and document classification},
booktitle = {in Proceedings of the 2nd International Symposium on Information Geometry and its Applications},
year = {2005}
}
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Abstract
Abstract. High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. Typical approaches to modeling such data involve, either explicitly or implicitly, arbitrary geometric assumptions. In this paper, we review a framework introduced by Lebanon and Lafferty that is based on Čencov’s theorem for obtaining a coherent geometry for data. The framework enables adaptation of popular models to the new geometry and in the context of text classification yields superior performance with respect to classification error rate on held out data. The framework demonstrates how information geometry may be applied to modeling high dimensional structured data and points at new directions for future research. 1.
Citations
| 357 | Distance metric learning, with application to clustering with side-information - Xing, Ng, et al. - 2003 |
| 194 | Methods of information geometry - Amari, Nagaoka - 2000 |
| 66 | Boosting and maximum likelihood for exponential models - Lebanon, Lafferty |
| 63 | Diffusion kernels on statistical manifolds - Lafferty, Lebanon - 2005 |
| 38 | Statistical Decision Rules and Optimal Inference - Čencov - 1982 |
| 9 | An extended Cencov characterization of the information metric - Campbell - 1986 |
| 7 | Axiomatic geometry of conditional models - Lebanon - 2005 |
| 4 | Riemannian geometry and statistical machine learning - Lebanon - 2005 |
| 4 | Hyperplane margin classifiers on the multinomial manifold - Lebanon, Lafferty |
| 1 | Metric learning for text classification - Lebanon |
