Learning and Data Selection in Big Datasets
Abstract
Finding a dataset of minimal cardinality to characterize the optimal parameters of a model is of paramount importance in machine learning and distributed optimization over a network. This paper investigates the compressibility of large datasets. More specifically, we propose a framework that jointly learns the input-output mapping as well as the most representative samples of the dataset (sufficient dataset). Our analytical results show that the cardinality of the sufficient dataset increases sub-linearly with respect to the original dataset size. Numerical evaluations of real datasets reveal a large compressibility, up to 95%, without a noticeable drop in the learnability performance, measured by the generalization error.
Cite
Text
Ghadikolaei et al. "Learning and Data Selection in Big Datasets." International Conference on Machine Learning, 2019.Markdown
[Ghadikolaei et al. "Learning and Data Selection in Big Datasets." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/ghadikolaei2019icml-learning/)BibTeX
@inproceedings{ghadikolaei2019icml-learning,
title = {{Learning and Data Selection in Big Datasets}},
author = {Ghadikolaei, Hossein Shokri and Ghauch, Hadi and Fischione, Carlo and Skoglund, Mikael},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {2191-2200},
volume = {97},
url = {https://mlanthology.org/icml/2019/ghadikolaei2019icml-learning/}
}