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PR
2008
129views more  PR 2008»
13 years 4 months ago
A comparison of generalized linear discriminant analysis algorithms
7 Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled p...
Cheong Hee Park, Haesun Park
IJON
2010
178views more  IJON 2010»
13 years 2 months ago
An empirical study of two typical locality preserving linear discriminant analysis methods
: Laplacian Linear Discriminant Analysis (LapLDA) and Semi-supervised Discriminant Analysis (SDA) are two recently proposed LDA methods. They are developed independently with the a...
Lishan Qiao, Limei Zhang, Songcan Chen
CVPR
2008
IEEE
14 years 6 months ago
A unified framework for generalized Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is one of the wellknown methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope w...
Shuiwang Ji, Jieping Ye
TNN
2008
105views more  TNN 2008»
13 years 4 months ago
Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection
Abstract--High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the curse-of-dimensionality. Linear discriminant analysis (LDA) is ...
Shuiwang Ji, Jieping Ye
SIAMMAX
2010
189views more  SIAMMAX 2010»
12 years 11 months ago
Fast Algorithms for the Generalized Foley-Sammon Discriminant Analysis
Linear Discriminant Analysis (LDA) is one of the most popular approaches for feature extraction and dimension reduction to overcome the curse of the dimensionality of the high-dime...
Lei-Hong Zhang, Li-Zhi Liao, Michael K. Ng