GD-RDA: A New Regularized Discriminant Analysis for High-Dimensional Data-Reference-Cited by-同舟云学术

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GD-RDA: A New Regularized Discriminant Analysis for High-Dimensional Data

Published:2017-11 Issue:11 Volume:24 Page:1099-1111
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Zhou Yan¹,Zhang Baoxue²,Li Gaorong³,Tong Tiejun⁴,Wan Xiang⁵

Affiliation:

1. College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, ShenZhen, China.

2. School of Statistics, Capital University of Economics and Business, Beijing, China.

3. Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing, China.

4. Department of Mathematics, Hong Kong Baptist University, Hong Kong, China.

5. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modelling and Simulation

Link

http://www.liebertpub.com/doi/pdf/10.1089/cmb.2017.0029

Reference27 articles.

1. Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations

2. Variable Selection in Cell Classification Problems: A Strategy Based on Independent Component Analysis

3. Learning from imbalanced data in surveillance of nosocomial infection

4. Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays

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