A Least Square Support Vector Machine Regression Method Based on Kernel Partial Least Square Feature Extraction

We apply kernel partial least square(KPLS) to least square support vector machines (LSSVM) for feature extraction. The original inputs are firstly mapped into a high dimensional feature space, then score vectors are calculated in high dimensional feature space so that dimensions of the sample are reduced. Experimental results show that LSSVM by feature extraction using KPLS performs much better than that without feature extraction. In comparison with PLS, there is also superior performance in KPLS.