Augmented Leverage Score Sampling with Bounds
Abstract
We introduce a modification to the well studied leverage score sampling algorithm which takes into account data scale, called the augmented leverage score , and introduce an initial error bound proof for the case of deterministic sampling – which to our knowledge is the first bound for this augmented leverage score. We discuss the implications of the error bounds proof and present an empirical evaluation of the proposed augmented leverage score performance on the column subsample selection problem (CSSP) as compared to the traditional leverage score and other methods in both a deterministic and probabilistic sampling paradigm. We show that the augmentation of the leverage score improves the empirical performance on CSSP significantly for many kinds of data.
Cite
Text
Perry and Whitaker. "Augmented Leverage Score Sampling with Bounds." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46227-1_34Markdown
[Perry and Whitaker. "Augmented Leverage Score Sampling with Bounds." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/perry2016ecmlpkdd-augmented/) doi:10.1007/978-3-319-46227-1_34BibTeX
@inproceedings{perry2016ecmlpkdd-augmented,
title = {{Augmented Leverage Score Sampling with Bounds}},
author = {Perry, Daniel J. and Whitaker, Ross T.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {543-558},
doi = {10.1007/978-3-319-46227-1_34},
url = {https://mlanthology.org/ecmlpkdd/2016/perry2016ecmlpkdd-augmented/}
}