Indefinite kernels in least squares support vector machines and principal component analysis

DOI10.1016/j.acha.2016.09.001zbMath1407.68395OpenAlexW2520536546MaRDI QIDQ2397170

Andreas Maier, Johan A. K. Suykens, Joachim Hornegger, Xiao Lin Huang

Publication date: 29 May 2017

Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.acha.2016.09.001

zbMATH Keywords

classificationkernel principal component analysisleast squares support vector machineindefinite kernel

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

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Uses Software

• UCI-ml
• LIBSVM
• LS-SVMlab

Cites Work

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• Least square regression with indefinite kernels and coefficient regularization
• Regularization networks with indefinite kernels
• Invariant kernel functions for pattern analysis and machine learning
• 10.1162/15324430260185592
• Indefinite Kernel Discriminant Analysis
• Chaos control using least-squares support vector machines
• Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
• Indefinite Proximity Learning: A Review
• Encyclopedia of Distances