The Blind Leading the Blind: Network-Based Location Estimation Under Uncertainty
Abstract
We propose a probabilistic method for inferring the geographical locations of linked objects, such as users in a social network. Unlike existing methods, our model does not assume that the exact locations of any subset of the linked objects, like neighbors in a social network, are known. The method efficiently leverages prior knowledge on the locations, resulting in high geolocation accuracies even if none of the locations are initially known. Experiments are conducted for three scenarios: geolocating users of a location-based social network, geotagging historical church records, and geotagging Flickr photos. In each experiment, the proposed method outperforms two state-of-the-art network-based methods. Furthermore, the last experiment shows that the method can be employed not only to network-based but also to content-based location estimation.
Cite
Text
Malmi et al. "The Blind Leading the Blind: Network-Based Location Estimation Under Uncertainty." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23525-7_25Markdown
[Malmi et al. "The Blind Leading the Blind: Network-Based Location Estimation Under Uncertainty." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/malmi2015ecmlpkdd-blind/) doi:10.1007/978-3-319-23525-7_25BibTeX
@inproceedings{malmi2015ecmlpkdd-blind,
title = {{The Blind Leading the Blind: Network-Based Location Estimation Under Uncertainty}},
author = {Malmi, Eric and Solin, Arno and Gionis, Aristides},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2015},
pages = {406-421},
doi = {10.1007/978-3-319-23525-7_25},
url = {https://mlanthology.org/ecmlpkdd/2015/malmi2015ecmlpkdd-blind/}
}