On feature scaling

Abstract

Preprocessing data is an important step before any data analysis. In this thesis, 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.

Date of Award	2016
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology