Kernel discriminant analysis for regression problems

DOI10.1016/J.PATCOG.2011.11.006zbMath1233.68190OpenAlexW1975417544MaRDI QIDQ763381

Nojun Kwak

Publication date: 9 March 2012

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.11.006

zbMATH Keywords

discriminant analysisfeature extractionregressiondimensionality reductionkernel trickLDAr

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (2)

A Rayleigh-Ritz style method for large-scale discriminant analysis ⋮ Maximum relevance minimum common redundancy feature selection for nonlinear data

Uses Software

• UCI-ml
• alr3
• KPCA plus LDA
• SVM

Cites Work

• Sliced Inverse Regression for Dimension Reduction
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Kernel PCA for feature extraction and de-noising in nonlinear regression
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