Adaptive Scaling
Machine Learning
2017-09-05 v1 Applications
Abstract
Preprocessing data is an important step before any data analysis. In this paper, we focus on one particular aspect, namely scaling or normalization. We analyze various scaling methods in common use and study their effects on different statistical learning models. We will propose a new two-stage scaling method. First, we use some training data to fit linear regression model and then scale the whole data based on the coefficients of regression. Simulations are conducted to illustrate the advantages of our new scaling method. Some real data analysis will also be given.
Cite
@article{arxiv.1709.00566,
title = {Adaptive Scaling},
author = {Ting Li and Bingyi Jing and Ningchen Ying and Xianshi Yu},
journal= {arXiv preprint arXiv:1709.00566},
year = {2017}
}