Adaptive Scaling

09/02/2017
by   Ting Li, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/01/2022

Weighted Scaling Approach for Metabolomics Data Analysis

Systematic variation is a common issue in metabolomics data analysis. Th...
research
07/28/2014

Dynamic Feature Scaling for Online Learning of Binary Classifiers

Scaling feature values is an important step in numerous machine learning...
research
01/20/2023

Collected Notes on Aldrich-Mckelevey Scaling

Aldrich-McKelvey scaling is a method for correcting differential item fu...
research
03/17/2021

Differential analysis in Transcriptomic: The strength of randomly picking 'reference' genes

Transcriptomic analysis are characterized by being not directly quantita...
research
06/07/2020

Sources of high leverage in linear regression model

Some reasons for high leverage are analytically investigated by decompos...
research
08/01/2019

Evaluating Perceptual Bias During Geometric Scaling of Scatterplots

Scatterplots are frequently scaled to fit display areas in multi-view an...
research
01/22/2021

Linear Regression with Distributed Learning: A Generalization Error Perspective

Distributed learning provides an attractive framework for scaling the le...

Please sign up or login with your details

Forgot password? Click here to reset