The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Regularized linear discriminant analysis and its application in microarrays
Abstract
In this paper, we introduce a modified version of linear discriminant analysis, called the "shrunken centroids regularized discriminant analysis" (SCRDA). This method generalizes the idea of the "nearest shrunken centroids" (NSC) (Tibshirani and others, 2003) into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, it is shown that this method performs very well in multivariate classification problems, often outperforms the PAM method (using the NSC algorithm) and can be as competitive as the support vector machines classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for this method (named "rda") is available on CRAN (http://www.r-project.org) for download and testing.
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Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Self-citationThe Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
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Cited by
Using deep transfer learning for image-based plant disease identification
Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105393 · Cited by 1,052
Machine learning and its applications to biology
Tarca, A. L., Carey, V. J., Chen, X.-W., Romero, R., & Drăghici, S. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030116 · Cited by 656
CCA: An R Package to Extend Canonical Correlation Analysis
Gonzalez, I., Déjean, S., Martin, P., & Baccini, A. (2008) · Journal of Statistical Software · doi:10.18637/jss.v023.i12 · Cited by 377
Frantz, A. L., Rogier, E. W., Weber, C. R., Shen, L., Cohen, D. A., Fenton, L. A., Bruno, M. E. C., & Kaetzel, C. S. (2012) · Mucosal Immunology · doi:10.1038/mi.2012.23 · Cited by 230
Authorship Attribution for Social Media Forensics
Rocha, A., Scheirer, W. J., Forstall, C. W., Cavalcante, T., Theophilo, A., Shen, B., Carvalho, A. R. B., & Stamatatos, E. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2603960 · Cited by 222
Metadata
- Authors
- Guo, Y., Hastie, T., & Tibshirani, R.
- Venue
- Biostatistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-04-07
- Volume
- 8
- Issue
- 1
- Pages
- 86-100
- ISSN
- 1465-4644 (ISSN-L)
- 1468-4357
- OpenAlex
- W2155423555
- Cited by
- 597
- References
- 2
- FWCI
- 5.28
- Type
- journal article
- Language
- en
- Open access
- closed
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