Label Propagation for Learning with Label Proportions
Machine Learning
2018-10-25 v1 Machine Learning
Abstract
Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare domain, it is a burden for a patient to keep a detailed diary of their daily routines, but often they will be amenable to provide higher level summaries of daily behavior. We present a novel and efficient graph-based algorithm that encourages local smoothness and exploits the global structure of the data, while preserving the `mass' of each bag.
Cite
@article{arxiv.1810.10328,
title = {Label Propagation for Learning with Label Proportions},
author = {Rafael Poyiadzi and Raul Santos-Rodriguez and Niall Twomey},
journal= {arXiv preprint arXiv:1810.10328},
year = {2018}
}
Comments
Accepted to MLSP 2018