Adaptive Scaling

TL;DR

This paper introduces a novel two-stage data scaling method that leverages linear regression coefficients to improve normalization, demonstrating its effectiveness through simulations and real data analysis.

Contribution

A new two-stage scaling approach is proposed, combining linear regression fitting with data normalization to enhance data preprocessing for statistical models.

Findings

The new scaling method outperforms traditional methods in simulations.

It improves model performance on real datasets.

The approach is computationally efficient and easy to implement.

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.