The article introduces a new method for monitoring processes called improved kernel principal component analysis. This method automatically selects the best kernel function for analyzing data, improving the accuracy of process monitoring. By using a combination of manifold learning and a formula called generalized Nystrom, the researchers were able to show that this improved method works well in numerical simulations and on a real-world process benchmark.