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» Kernel Optimization in Discriminant Analysis
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ML
2010
ACM
13 years 6 months ago
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled samples ...
Masashi Sugiyama, Tsuyoshi Idé, Shinichi Na...
CVPR
2010
IEEE
14 years 3 months ago
Pareto Discriminant Analysis
Linear Discriminant Analysis (LDA) is a popular tool for multiclass discriminative dimensionality reduction. However, LDA suffers from two major problems: (1) It only optimizes th...
Karim Abou-Moustafa, Fernando De la Torre, Frank F...
PRL
2010
209views more  PRL 2010»
13 years 2 months ago
Efficient update of the covariance matrix inverse in iterated linear discriminant analysis
For fast classification under real-time constraints, as required in many imagebased pattern recognition applications, linear discriminant functions are a good choice. Linear discr...
Jan Salmen, Marc Schlipsing, Christian Igel
CVPR
2005
IEEE
14 years 1 months ago
Nonlinear Face Recognition Based on Maximum Average Margin Criterion
This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum Average Margin Criterion (KMAMC), which has combined the idea of Support Vector Mac...
Baochang Zhang, Xilin Chen, Shiguang Shan, Wen Gao
ICML
2007
IEEE
14 years 8 months ago
Feature selection in a kernel space
We address the problem of feature selection in a kernel space to select the most discriminative and informative features for classification and data analysis. This is a difficult ...
Bin Cao, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng...