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PR
2008
161views more  PR 2008»
8 years 7 months ago
A study on three linear discriminant analysis based methods in small sample size problem
In this paper, we make a study on three Linear Discriminant Analysis (LDA) based methods: Regularized Discriminant Analysis (RDA), Discriminant Common Vectors (DCV) and Maximal Ma...
Jun Liu, Songcan Chen, Xiaoyang Tan
PR
2008
144views more  PR 2008»
8 years 7 months ago
Kernel quadratic discriminant analysis for small sample size problem
It is generally believed that quadratic discriminant analysis (QDA) can better fit the data in practical pattern recognition applications compared to linear discriminant analysis ...
Jie Wang, Konstantinos N. Plataniotis, Juwei Lu, A...
ICPR
2002
IEEE
9 years 8 months ago
Solving the Small Sample Size Problem of LDA
The small sample size problem is often encountered in pattern recognition. It results in the singularity of the within-class scatter matrix Sw in Linear Discriminant Analysis (LDA...
Rui Huang, Qingshan Liu, Hanqing Lu, Songde Ma
PAA
2002
8 years 6 months ago
Bagging, Boosting and the Random Subspace Method for Linear Classifiers
: Recently bagging, boosting and the random subspace method have become popular combining techniques for improving weak classifiers. These techniques are designed for, and usually ...
Marina Skurichina, Robert P. W. Duin
ACII
2005
Springer
8 years 9 months ago
A Novel Regularized Fisher Discriminant Method for Face Recognition Based on Subspace and Rank Lifting Scheme
The null space N(St) of total scatter matrix St contains no useful information for pattern classiļ¬cation. So, discarding the null space N(St) results in dimensionality reduction ...
Wen-Sheng Chen, Pong Chi Yuen, Jian Huang, Jian-Hu...
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