The Generalized Semi-Supervised Elastic-Net

Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 for references on the Joint Trained Elastic-Net.


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1.0.1 by Juan C. Laria, a year ago

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Authors: Juan C. Laria [aut, cre] , Line H. Clemmensen [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, methods, MASS

Depends on stats

Suggests knitr, rmarkdown, glmnet, Metrics, testthat

Linking to Rcpp, RcppArmadillo

See at CRAN