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» Optimally Regularised Kernel Fisher Discriminant Analysis
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ICPR
2008
IEEE
14 years 8 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
JMM2
2008
157views more  JMM2 2008»
13 years 7 months ago
Multiresolution Feature Based Fractional Power Polynomial Kernel Fisher Discriminant Model for Face Recognition
This paper presents a technique for face recognition which uses wavelet transform to derive desirable facial features. Three level decompositions are used to form the pyramidal mul...
Dattatray V. Jadhav, Jayant V. Kulkarni, Raghunath...
ICDAR
2009
IEEE
14 years 2 months ago
Fisher Kernels for Handwritten Word-spotting
The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to a...
Florent Perronnin, José A. Rodríguez...
PAMI
2011
13 years 2 months ago
Kernel Optimization in Discriminant Analysis
— Kernel mapping is one of the most used approaches to intrinsically derive nonlinear classifiers. The idea is to use a kernel function which maps the original nonlinearly separ...
Di You, Onur C. Hamsici, Aleix M. Martínez
JMLR
2010
143views more  JMLR 2010»
13 years 2 months ago
Regularized Discriminant Analysis, Ridge Regression and Beyond
Fisher linear discriminant analysis (FDA) and its kernel extension--kernel discriminant analysis (KDA)--are well known methods that consider dimensionality reduction and classific...
Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jor...