SelectParams {biClassify}R Documentation

Generates parameters.

Description

Generates parameters to be used in sparse kernel optimal scoring.

Usage

SelectParams(TrainData, TrainCat, Sigma = NULL, Gamma = NULL,
  Epsilon = 1e-05)

Arguments

TrainData

(n x p) Matrix of training data with numeric features. Cannot have missing values.

TrainCat

(n x 1) Vector of class membership. Values must be either 1 or 2.

Sigma

Scalar Gaussian kernel parameter. Default set to NULL and is automatically generated if user-specified value not provided. Must be > 0. User-specified parameters must satisfy hierarchical ordering.

Gamma

Scalar ridge parameter used in kernel optimal scoring. Default set to NULL and is automatically generated if user-specified value not provided. Must be > 0. User-specified parameters must satisfy hierarchical ordering.

Epsilon

Numerical stability constant with default value 1e-05. Must be > 0 and is typically chosen to be small.

Details

Generates the gaussian kernel, ridge, and sparsity parameters for use in sparse kernel optimal scoring using the methods presented in [Lapanowski and Gaynanova, preprint]. The Gaussian kernel parameter is generated using five-fold cross-validation of the misclassification error rate aross the .05, .1, .2, .3, .5 quantiles of squared-distances between groups. The ridge parameter is generated using a stabilization technique developed in Lapanowski and Gaynanova (2019). The sparsity parameter is generated by five-fold cross-validation over a logarithmic grid of 20 values in an automatically-generated interval.

Value

A list of

Sigma

Gaussian kernel parameter.

Gamma

Ridge Parameter.

Lambda

Sparsity parameter.

References

Lancewicki, Tomer. "Regularization of the kernel matrix via covariance matrix shrinkage estimation." arXiv preprint arXiv:1707.06156 (2017).

Lapanowski, Alexander F., and Gaynanova, Irina. “Sparse feature selection in kernel discriminant analysis via optimal scoring”, Artificial Intelligence and Statistics, 2019.

Examples


Sigma <- 1.325386  #Set parameter values equal to result of SelectParam.
Gamma <- 0.07531579 #Speeds up example

TrainData <- KOS_Data$TrainData
TrainCat <- KOS_Data$TrainCat

SelectParams(TrainData = TrainData , 
             TrainCat = TrainCat, 
             Sigma = Sigma, 
             Gamma = Gamma)


[Package biClassify version 1.3 Index]