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AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
13 years 11 months ago
A Discriminant Analysis for Undersampled Data
One of the inherent problems in pattern recognition is the undersampled data problem, also known as the curse of dimensionality reduction. In this paper a new algorithm called pai...
Matthew Robards, Junbin Gao, Philip Charlton
ICDM
2003
IEEE
115views Data Mining» more  ICDM 2003»
13 years 10 months ago
A new optimization criterion for generalized discriminant analysis on undersampled problems
Jieping Ye, Ravi Janardan, Cheong Hee Park, Haesun...
PRL
2006
98views more  PRL 2006»
13 years 4 months ago
Data complexity assessment in undersampled classification of high-dimensional biomedical data
Regularized linear classifiers have been successfully applied in undersampled, i.e. small sample size/high dimensionality biomedical classification problems. Additionally, a desig...
Richard Baumgartner, Ray L. Somorjai
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
ICML
2006
IEEE
14 years 5 months ago
Null space versus orthogonal linear discriminant analysis
Dimensionality reduction is an important pre-processing step for many applications. Linear Discriminant Analysis (LDA) is one of the well known methods for supervised dimensionali...
Jieping Ye, Tao Xiong