Functional principal component regression versus support vector regression for the analysis of spectroscopic data‎

‎The most popular technique for functional data analysis is the functional principal component approach‎, ‎which is also an important tool for dimension reduction‎. ‎Support vector regression is branch of machine learning and strong tool for data analysis‎. ‎In this paper by using the method of functional principal component regression based on the second derivative penalty‎, ‎ridge and lasso and support vector regression with four kernels (linear‎, ‎polynomial‎, ‎sigmoid and radial) in spectroscopic data‎, ‎the dependent variable on the predictor variables was modeled‎. ‎According to the obtained results‎, ‎based on the proposed criteria for evaluating the goodness of fit‎, ‎support vector regression with linear kernel and error equal to $0.2$ has had the most appropriate fit to the data set‎.

‎