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» Manifold Discriminant Analysis
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PAMI
2011
13 years 4 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
IEEEMM
2007
146views more  IEEEMM 2007»
13 years 10 months ago
Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis
— Microarray technology offers a high throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small s...
Yijuan Lu, Qi Tian, Maribel Sanchez, Jennifer L. N...
ICML
2006
IEEE
14 years 10 months ago
Optimal kernel selection in Kernel Fisher discriminant analysis
In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance...
Seung-Jean Kim, Alessandro Magnani, Stephen P. Boy...
ICPR
2008
IEEE
14 years 4 months ago
Linear discriminant analysis for data with subcluster structure
Linear discriminant analysis (LDA) is a widely-used feature extraction method in classification. However, the original LDA has limitations due to the assumption of a unimodal str...
Haesun Park, Jaegul Choo, Barry L. Drake, Jinwoo K...
ICPR
2008
IEEE
14 years 4 months ago
Boosting performance for 2D Linear Discriminant Analysis via regression
Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become signi...
Nam Nguyen, Wanquan Liu, Svetha Venkatesh