Kernel-based Information Criterion
This addresses model selection for regression practitioners, offering an incremental improvement over existing criteria.
The paper tackles model selection in regression analysis by introducing the Kernel-based Information Criterion (KIC), which uses a kernel-based complexity measure to compute parameter interdependency and select more robust regressors, showing superior performance compared to methods like LOOCV and GPR on simulated and real data.
This paper introduces Kernel-based Information Criterion (KIC) for model selection in regression analysis. The novel kernel-based complexity measure in KIC efficiently computes the interdependency between parameters of the model using a variable-wise variance and yields selection of better, more robust regressors. Experimental results show superior performance on both simulated and real data sets compared to Leave-One-Out Cross-Validation (LOOCV), kernel-based Information Complexity (ICOMP), and maximum log of marginal likelihood in Gaussian Process Regression (GPR).