An online NIPALS algorithm for Partial Least Squares
Alexander E. Stott, Sithan Kanna, Danilo P. Mandic, William T. Pike
Abstract
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.
BibTeX
@inproceedings{icassp2017_anonlinenipalsal,
title = {An online NIPALS algorithm for Partial Least Squares},
author = {Alexander E. Stott and Sithan Kanna and Danilo P. Mandic and William T. Pike},
booktitle = {ICASSP 2017},
year = {2017}
}