Elastic-Net Regression Algorithm Based on Multi-Scale Gaussian Kernel
Yongli Xu, Zhenjun Yang · 2014
This paper proposes an elastic-net algorithm based on multi-scale Gaussian kernels to deal with the approximation of regression function. We use Gaussian kernels with different kernel width to approximate the high and low frequency components of the regression function; then weighted L1 norm and L2 norm of the prediction function utilized as regularization term. In the simulation experiments, multiple Gaussian kernels based elastic-net obtains less prediction error and better sparse performance than single Gaussian kernel based elastic-net. In addition, multiple Gaussian kernels based elastic-net can precisely predict the high and low frequency components of objective function.