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Dimension selection for feature selection and dimension reduction with principal and independent component analysis
Inge Koch
, Kanta Naito
Research output
:
Contribution to journal
›
Article
›
peer-review
24
Citations (Scopus)
Overview
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Dive into the research topics of 'Dimension selection for feature selection and dimension reduction with principal and independent component analysis'. Together they form a unique fingerprint.
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Mathematics
Principal Components
100%
Independent Component
100%
Simulation Study
50%
Method Performs
50%
Skewness
50%
Kurtosis
50%
Dimensional Space
50%
Real Data
50%
Dimensional Data
50%
Principal Component Analysis
50%
Engineering
Feature Extraction
100%
Independent Component Analysis
100%
Principal Components
100%
Dimensional Space
33%
Real Data
33%
Dimensional Data
33%
Component Analysis
33%