Learning the kernel matrix by maximizing a KFD-based class separability criterion

DOI10.1016/j.patcog.2006.12.031zbMath1111.68628OpenAlexW2052601987MaRDI QIDQ882223

Hong Chang, Dit-Yan Yeung, Guang Dai

Publication date: 23 May 2007

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2006.12.031

zbMATH Keywords

face recognitionFisher discriminant criterionkernel learningkernel Fisher discriminant

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (5)

Learning with infinitely many features ⋮ Multiple kernel clustering based on centered kernel alignment ⋮ Ideal regularization for learning kernels from labels ⋮ Scaling the kernel function based on the separating boundary in input space: a data-dependent way for improving the performance of kernel methods ⋮ Evolutionary combination of kernels for nonlinear feature transformation

Uses Software

• FERET

Cites Work

• 10.1162/153244304322765649
• Choosing multiple parameters for support vector machines
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