Adaptive Scaling
This work addresses a specific preprocessing bottleneck in statistical learning, but it appears incremental as it builds on known scaling techniques.
The paper tackles the problem of data scaling in preprocessing by proposing a new two-stage method that uses linear regression coefficients from training data to scale the entire dataset, with simulations and real data analysis showing advantages over existing methods.
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.