Influence of feature scaling on convergence of gradient iterative algorithm

Feature scaling is a method to unify self-variables or feature ranges in data. In data processing, it is usually used in data pre-processing. Because in the original data, the range of variables is very different. Feature scaling is a necessary step in the calculation of stochastic gradient descent....

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of physics. Conference series Ročník 1213; číslo 3; s. 32021 - 32025
Hlavný autor: Wan, Xing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 01.06.2019
Predmet:
Convergence
Data processing
Iterative algorithms
Iterative methods
Scaling
ISSN:1742-6588, 1742-6596
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Feature scaling is a method to unify self-variables or feature ranges in data. In data processing, it is usually used in data pre-processing. Because in the original data, the range of variables is very different. Feature scaling is a necessary step in the calculation of stochastic gradient descent. This paper takes the computer hardware data set maintained by UCI as an example, and compares the influence of normalization method and interval scaling method on the convergence of stochastic gradient descent by algorithm simulation. The result of study has a certain value on feature scaling.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1213/3/032021