Non-Metric Partial Least Squares

By Giorgio Russolillo. 2012

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In this paper I review covariance-based Partial Least Squares (PLS) methods, focusing on common features of their respective algorithms and optimization criteria. I then show how these algorithms can be ad- justed for use as optimal scaling tools. Three new PLS-type algorithms are proposed for the analysis of one, two or several blocks of variables: the Non- Metric NIPALS, the Non-Metric PLS Regression and the Non-Metric PLS Path Modeling, respectively. These algorithms extend the applicability of PLS methods to data measured on different measurement scales, as well as to variables linked by non-linear relationships.

Subfields: Analytical Chemistry, Industrial and Manufacturing Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1673310589176de24dbf6dc6e44543be5b5f7435