An online NIPALS algorithm for Partial Least Squares

Partial Least Squares (PLS) has been gaining popularity as a multivariate data analysis tool due to its ability to cater for noisy, collinear and incomplete data-sets. However, most PLS solutions are designed as block-based algorithms, rendering them unsuitable for environments with streaming data and non-stationary statistics. To this end, we propose an online version of the nonlinear iterative PLS (NIPALS) algorithm, based on a recursive computation of covariance matrices and gradient-based techniques to compute eigenvectors of the relevant matrices. Simulations over synthetic data show that the regression coefficients from the proposed online PLS algorithm converge to those of its block-based counterparts.