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....

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Veröffentlicht in:Journal of physics. Conference series Jg. 1213; H. 3; S. 32021 - 32025
1. Verfasser: Wan, Xing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.06.2019
Schlagworte:
Convergence
Data processing
Iterative algorithms
Iterative methods
Scaling
ISSN:1742-6588, 1742-6596
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1213/3/032021