I want to use dimension reduction method for a high dimensional data set Is there any possible way to assess the "non-linearity" of the data first to give me the insight of whether I should use linear method (e.g. PCA) or nonlinear method (e.g. NLPCA)? I read several literatures and neither of them could provide a justification of when should I use linear of nonlinear method. They always use examples of "swiss roll" to indicate a data set which I should use nonlinear method. But for practical case, I cannot visualize a high dimensional data set first before I choose the method. To put it more straightforward, I want to have a test of the data set first and the result will tell me whether the nonlinear method is more suitable for this data set or vice versus.