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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
20
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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Computer Science
Existing Method
100%
Component Analysis
100%
Principal Component
100%
Independent Component
100%
Pattern Classification
50%
Simulation Study
50%
Real Data Sets
50%
Feature Selection
50%
Feature Dimension
50%
High Dimensional Data
50%
Lower Dimensional Space
50%
Component Score
50%
Mathematics
Principal Component
100%
Independent Component
100%
Skewness
100%
Kurtosis
100%
Principal Component Analysis
50%
Simulation Study
50%
Method Performs
50%