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» Generalized Discriminant Analysis Using a Kernel Approach
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CVPR
2011
IEEE
13 years 2 months ago
Graph Embedding Discriminant Analysis on Grassmannian Manifolds for Improved Image Set Matching
A convenient way of dealing with image sets is to represent them as points on Grassmannian manifolds. While several recent studies explored the applicability of discriminant analy...
Mehrtash Harandi, Sareh, Shirazi (National ICT Aus...
IJCNN
2008
IEEE
14 years 12 days ago
Feature selection based on kernel discriminant analysis for multi-class problems
— We propose a feature selection criterion based on kernel discriminant analysis (KDA) for an -class problem, which finds eigenvectors on which the projected class data are loca...
Tsuneyoshi Ishii, Shigeo Abe
PR
2007
139views more  PR 2007»
13 years 5 months ago
Learning the kernel matrix by maximizing a KFD-based class separability criterion
The advantage of a kernel method often depends critically on a proper choice of the kernel function. A promising approach is to learn the kernel from data automatically. In this p...
Dit-Yan Yeung, Hong Chang, Guang Dai
CVPR
2007
IEEE
14 years 8 months ago
Sparse Kernels for Bayes Optimal Discriminant Analysis
Discriminant Analysis (DA) methods have demonstrated their utility in countless applications in computer vision and other areas of research ? especially in the C class classificat...
Aleix M. Martínez, Onur C. Hamsici
JMLR
2006
136views more  JMLR 2006»
13 years 6 months ago
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
In this paper we consider a novel Bayesian interpretation of Fisher's discriminant analysis. We relate Rayleigh's coefficient to a noise model that minimises a cost base...
Tonatiuh Peña Centeno, Neil D. Lawrence