A method to extract non-linear principal components of large datasets - an application in skill transfer

Abstract

This article presents a methodology to extract principal components of large datasets, called C-NLPCA (cascaded nonlinear principal component analysis), and evaluates its use in the extraction of main human movements in image series, aiming for the development of methodologies and techniques for skill transfer from humans to robotic/virtual agents. The C-NLPCA is an original data multivariate analysis method based on the NLPCA (nonlinear principal component analysis). This method has as main features the capability of taking principal variability components from a large set of data, considering the existence of possible nonlinear relations among them. The proposed method is used to extract principal movements from video sequence of human activities, which can be reconstructed in cybernetic and robotic contexts. Aiming for the validation of the method a human moving hand test is presented, where C-NLPCA is applied and the patterns of the obtained movements are confronted with traditional linear techniques.