Perturbation LDA: Learning the difference between the class empirical mean and its expectation

WS Zheng, PC Yuen, SZ Li - Pattern Recognition, 2009 - Elsevier
Fisher's linear discriminant analysis (LDA) is popular for dimension reduction and extraction
of discriminant features in many pattern recognition applications, especially biometric …
被引用次数：27 相关文章

Perturbation LDA: Learning the difference between the class empirical mean and its expectation

WS Zheng, JH Lai, PC Yuen, SZ Li - Pattern Recognition, 2009 - ui.adsabs.harvard.edu
Fisher's linear discriminant analysis (LDA) is popular for dimension reduction and extraction
of discriminant features in many pattern recognition applications, especially biometric …
[PDF] psu.edu

[PDF][PDF] Perturbation LDA: Learning the difference between the class empirical mean and its expectation

WS Zhenga, JH Laib, PC Yuend, SZ Lie - Pattern Recognition, 2009 - Citeseer
Data in some applications such as biometric learning are of high dimension, while available
samples for each class are always limited. In view of this, dimension reduction is always …
[PDF] isee-ai.cn

[PDF][PDF] Perturbation LDA: Learning the difference between the class empirical mean and its expectation

WS Zhenga, JH Laib, PC Yuend, SZ Lie - Pattern Recognition, 2009 - isee-ai.cn
Data in some applications such as biometric learning are of high dimension, while available
samples for each class are always limited. In view of this, dimension reduction is always …

[引用][C] Perturbation LDA: Learning the difference between the class empirical mean and its expectation

WS ZHENG, JH LAI, PC YUEN, SZ LI - Pattern recognition, 2009 - pascal-francis.inist.fr
Perturbation LDA : Learning the difference between the class empirical mean and its
expectation CNRS Inist Pascal-Francis CNRS Pascal and Francis Bibliographic Databases …

Perturbation LDA: Learning the difference between the class empirical mean and its expectation

WS Zheng, JH Lai, PC Yuen, SZ Li - Pattern Recognition, 2009 - infona.pl
Fisher's linear discriminant analysis (LDA) is popular for dimension reduction and extraction
of discriminant features in many pattern recognition applications, especially biometric …

Perturbation LDA: Learning the difference between the class empirical mean and its expectation

WS Zheng, JH Lai, PC Yuen, SZ Li - Pattern Recognition, 2009 - dl.acm.org
Fisher's linear discriminant analysis (LDA) is popular for dimension reduction and extraction
of discriminant features in many pattern recognition applications, especially biometric …
[PDF] isee-ai.cn

[PDF][PDF] Perturbation LDA: Learning the difference between the class empirical mean and its expectation

WS Zhenga, JH Laib, PC Yuend, SZ Lie - Pattern Recognition, 2009 - isee-ai.cn
Data in some applications such as biometric learning are of high dimension, while available
samples for each class are always limited. In view of this, dimension reduction is always …
[PDF] psu.edu

[PDF][PDF] Perturbation LDA: Learning the Difference between the Class Empirical Mean and Its Expectation

WS Zhenga, JH Laib, PC Yuend, SZ Lie - Citeseer
Abstract Fisher's Linear Discriminant Analysis (LDA) is popular for dimension reduction and
extraction of discriminant features in many pattern recognition applications, especially …
[PDF] isee-ai.cn

[PDF][PDF] Perturbation LDA: Learning the Difference between the Class Empirical Mean and Its Expectation

WS Zhenga, JH Laib, PC Yuend, SZ Lie - isee-ai.cn
Abstract Fisher's Linear Discriminant Analysis (LDA) is popular for dimension reduction and
extraction of discriminant features in many pattern recognition applications, especially …