A general framework for dimensionality reduction of K-means clustering

DOI10.1007/s00357-019-09342-4OpenAlexW2969730807WikidataQ127341425 ScholiaQ127341425MaRDI QIDQ2220690

Tong Wu, Muhan Guo, Feiping Nie, Yanni Xiao

Publication date: 25 January 2021

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00357-019-09342-4

zbMATH Keywords

dimensionality reductionK-means clusteringtrace ratio LDA

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Uses Software

• JAFFE

Cites Work

• A supervised non-linear dimensionality reduction approach for manifold learning
• Semi-supervised classification based on random subspace dimensionality reduction
• A novel supervised dimensionality reduction algorithm: graph-based Fisher analysis
• A two-dimensional neighborhood preserving projection for appearance-based face recognition
• Improved discriminate analysis for high-dimensional data and its application to face recognition
• Semi-supervised local Fisher discriminant analysis for dimensionality reduction
• Semi-supervised orthogonal discriminant analysis via label propagation
• Semi-supervised double sparse graphs based discriminant analysis for dimensionality reduction
• Discriminative sparse flexible manifold embedding with novel graph for robust visual representation and label propagation
• Semi-supervised linear discriminant analysis for dimension reduction and classification
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