Comprehensive Evaluation on Regional Economic and Social Development based on Kernel Principal Composition Analysis

To solve the drawbacks of principal composition analysis (PCA) used to analyze nonlinear problem in comprehensive evaluation with multiple indicators, kernel principal composition analysis (KPCA) is introduced. By using the kernel functions, one can efficiently calculate principal compositions in high dimensional feature spaces, related in input space by some nonlinear map. By choosing appropriate parameters, the maximum eigenvalue contributes above or nearly 85%, avoiding the different array as a result of many principal compositions. An example is presented to illustrate that KPCA has a high objectivity.