Data Association with Gaussian Processes
Abstract
The data association problem is concerned with separating data coming from different generating processes, for example when data comes from different data sources, contain significant noise, or exhibit multimodality. We present a fully Bayesian approach to this problem. Our model is capable of simultaneously solving the data association problem and the induced supervised learning problem. Underpinning our approach is the use of Gaussian process priors to encode the structure of both the data and the data associations. We present an efficient learning scheme based on doubly stochastic variational inference and discuss how it can be applied to deep Gaussian process priors.
Cite
Text
Kaiser et al. "Data Association with Gaussian Processes." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46147-8_33Markdown
[Kaiser et al. "Data Association with Gaussian Processes." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/kaiser2019ecmlpkdd-data/) doi:10.1007/978-3-030-46147-8_33BibTeX
@inproceedings{kaiser2019ecmlpkdd-data,
title = {{Data Association with Gaussian Processes}},
author = {Kaiser, Markus and Otte, Clemens and Runkler, Thomas A. and Ek, Carl Henrik},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2019},
pages = {548-564},
doi = {10.1007/978-3-030-46147-8_33},
url = {https://mlanthology.org/ecmlpkdd/2019/kaiser2019ecmlpkdd-data/}
}