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» Generalized Discriminant Analysis Using a Kernel Approach
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82
Voted
ICPR
2008
IEEE
15 years 10 months ago
Generalized Nonlinear Discriminant Analysis
A Generalized Nonlinear Discriminant Analysis (GNDA) method is proposed, which implements Fisher discriminant analysis in a nonlinear mapping space. Linear discriminant analysis i...
Hua Zhang, Li Zhang, Licheng Jiao, Weida Zhou
CVPR
2009
IEEE
16 years 4 months ago
Volterrafaces: Discriminant Analysis using Volterra Kernels
In this paper we present a novel face classification system where we represent face images as a spatial arrangement of image patches, and seek a smooth non-linear functional map...
Ritwik Kumar, Arunava Banerjee, Baba C. Vemuri
72
Voted
CVPR
2006
IEEE
15 years 11 months ago
Kernel Uncorrelated and Orthogonal Discriminant Analysis: A Unified Approach
Several kernel algorithms have recently been proposed for nonlinear discriminant analysis. However, these methods mainly address the singularity problem in the high dimensional fe...
Tao Xiong, Jieping Ye, Vladimir Cherkassky
97
Voted
CVPR
2005
IEEE
15 years 11 months ago
Fisher+Kernel Criterion for Discriminant Analysis
We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel Criterion. In addition, an eff...
Shu Yang, Shuicheng Yan, Dong Xu, Xiaoou Tang, Cha...
NIPS
2004
14 years 11 months ago
Efficient Kernel Discriminant Analysis via QR Decomposition
Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Re...
Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladi...