A method of dimensionality reduction by selection of components in principal component analysis for text classification

DOI10.2298/FIL1805499ZzbMath1499.62204OpenAlexW2912687981WikidataQ125995698 ScholiaQ125995698MaRDI QIDQ5024582

Guohe Li, Heng Zong, Yangwu Zhang

Publication date: 26 January 2022

Published in: Filomat (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2298/fil1805499z

zbMATH Keywords

dimensionality reductiontext classificationprincipal components analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Uses Software

• C4.5
• PRMLT

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Selection of components in principal component analysis: A comparison of methods
• Principal component analysis.
• Cutting-plane training of structural SVMs
• Principal components selection given extensively many variables
• A vector space model for automatic indexing
• Smooth twin support vector machines via unconstrained convex minimization
• Learning representations by back-propagating errors

This page was built for publication: A method of dimensionality reduction by selection of components in principal component analysis for text classification