Demonstration of Principal Component Analysis on TI-86

Abstract

We often measure a variety of features when attempting to perform classification. Principal component analysis (PCA) can assist the multivariate investigation by reducing dimensionality and by maximizing feature space variance. For demonstration, this paper shows the techniques for finding the improved feature space and it shows how to project data into this space, using the native commands of the TI-86 calculator.