sparseKOS-package: Implementation of sparse kernel optimal scoring

In aflapan/sparseKOS: Implementation of sparse kernel optimal scoring

Description

A package for non-linear binary classification with simultaneous sparse feature selection. This package implements an algorithm developed in [Lapanowski and Gaynanova, preprint] called sparse kernel optimal scoring (sparse KOS). It combines kernel discriminant analysis with sparse feature selection. The kernel discriminant analysis is done through a kernelized version of regularized optimal scoring, which is a regression-type problem. This package uses the Gaussian kernel. Sparse feature selection is accomplished by placing a weight lying in [-1,1] on each data feature, and then including a sparsity penalty on the weight vector. Sparse KOS alternates between solving the kernelized optimal scoring problem and minimzing a LASSO-type problem on a feature weight vector. This package also implements automatic kernel, ridge, and sparsity parameter selection methods.

Details

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Author(s)

Alexander F. Lapanowski and Irina Gaynanova

Maintainer: Alexander F. Lapanowski <[e-mail]>

References

Lapanowski, Alexander F., and Gaynanova, Irina “Sparse feature selection in kernel discriminant analysis via optimal scoring”. Preprint

Examples

SelectParams(Data = Data$TrainData,
             Cat = Data$CatTrain,
             Sigma = 1.325386,
             Gamma = 0.07531579)
             

Predict( X = Data$TestData,
         Data = Data$TrainData,
         Cat = Data$CatTrain, 
         Sigma = 1.325386,
         Gamma = 0.07531579, 
         Lambda = 0.002855275)

aflapan/sparseKOS documentation built on May 3, 2019, 5:23 p.m.