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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Vydáno v:Journal of physics. Conference series Ročník 1213; číslo 3; s. 32021 - 32025
Hlavní autor: Wan, Xing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.06.2019
Témata:
Convergence
Data processing
Iterative algorithms
Iterative methods
Scaling
ISSN:1742-6588, 1742-6596
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Abstract 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.
AbstractList 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.
Author Wan, Xing
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Cites_doi 10.1109/LSP.2004.833491
10.1613/jair.105
10.1109/ICASSP.1980.1171076
10.1109/TCOMM.2002.806518
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Snippet 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...
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StartPage 32021
SubjectTerms Convergence
Data processing
Iterative algorithms
Iterative methods
Scaling
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Title Influence of feature scaling on convergence of gradient iterative algorithm
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