Monitoring Diesel Fuels with Supervised Distance Preserving Projections and Local Linear Regression

In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection (SDPP) and, we investigate its applicability to monitoring material's properties from spectroscopic observations using Local Linear Regression (LLR). An experimental evaluation is conducted to show the performance of the SDPP and LLR and compare it with a number of state-of-the-art approaches for unsupervised and supervised dimensionality reduction. For the task, the results obtained on a benchmark problem consisting of a set of NIR spectra of diesel fuels and six different chemico-physical properties of those fuels are discussed. Based on the experimental results, the SDPP leads to accurate and parsimonious projections that can be effectively used in the design of estimation models based on local linear regression.