Spectroscopic monitoring of diesel fuels using Supervised Distance Preserving Projections

In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection and, we investigate its applicability to monitoring material's properties from spectroscopic observations. Motivated by continuity preservation, the SDPP is a linear projection method where the local geometry of the points in the low-dimensional subspace mimics the geometry of the points in the response space. Such a mapping facilitates an efficient regressor design and it may also uncover useful information for visualisation. An experimental evaluation is conducted to show the performance of the SDPP 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 is discussed. Based on the experimental results, the SDPP leads to accurate and parsimonious projections that can be used in the design of efficient regression models.