Application of dimensionality reduction to visualisation of high-throughput data and building of a classification model in formulated consumer product design

Abstract

Several dimensionality reduction techniques were applied to two data sets of consumer products formulations in order to infer their intrinsic structure and specific product design rules. High throughput experiments were used to generate the data sets of sufficient size. Supervised isometric feature mapping (S-Isomap) was combined with a k-nearest neighbours (k-NN) classifier and k-means clustering algorithm to perform categorization of viscosity of new formulations, not used to train the model. We compared prediction results of this approach with several well-established classification models. The results show the accuracy of the S-Isomap based approach to be superior and with a potential for further improvement. Compared with other dimensionality reduction techniques, applying S-Isomap has allowed for a superior visualization of category separation within the formulations, for the data sets used.