On the Choice of the Kernel Function in Kernel Discriminant Analysis Using Information Complexity

DOI10.1007/3-540-35978-8_2zbMath1435.62227OpenAlexW155772811WikidataQ57599382 ScholiaQ57599382MaRDI QIDQ3296969

Caterina Liberati, Furio Camillo, Hamparsum Bozdogan

Publication date: 2 July 2020

Published in: Studies in Classification, Data Analysis, and Knowledge Organization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/3-540-35978-8_2

zbMATH Keywords

kernel functionkernel methodlinear discriminant analysisquadratic loss functionlinear discriminant analysis (LDA)feature spacemultivariate methodskernel Fisher discriminant analysis (KFDA)

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Measures of information, entropy (94A17) Statistical aspects of information-theoretic topics (62B10)

Related Items (1)

Advances in credit scoring: combining performance and interpretation in kernel discriminant analysis

Uses Software

• STPRTool

Cites Work

• Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers.
• Akaike's information criterion and recent developments in information complexity
• On the information-based measure of covariance complexity and its application to the evaluation of multivariate linear models
• Information theoretic covariance complexity and its relation to pattern recognition
• Theory of Reproducing Kernels
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