Analysis of Nonlinear Partial Least Squares Algorithms

摘要

This paper presents an analysis of nonlinear extensions to Partial Least Squares (PLS) using error-based minimization techniques. The analysis revealed that such algorithms are maximizing the accuracy with which the response variables are predicted. Therefore, such algorithms are nonlinear reduced rank regression algorithms rather than nonlinear PLS algorithms.