From projection pursuit to other unsupervised chemometric techniques

The main goal of exploratory data analysis is to reveal clusters of objects, local changes of data density, outlying objects and/or influential sources of data variance. These different aspects of data exploration can sometimes be accomplished simultaneously with the use of one algorithm, the projection algorithm (PA). In this paper, the PA is described and discussed in detail. It is shown that this algorithm can be considered as a general platform to perform principal components analysis (PCA), robust PCA and independent component analysis (ICA). This goal is achieved by optimizing various different projection indices in the PA. Among these indices one can find entropy, variance and robust scale estimator. The present paper can be regarded as tutorial, aiming to provide a better understanding of the projection pursuit approaches (PPs). Copyright © 2007 John Wiley & Sons, Ltd.