A Non-iterative Partial Least Squares Algorithm for Supervised Learning with Collinear Data

S. Joe QIN*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

1 Citation (Scopus)


Partial least squares (PLS) has gained popularity in many domains such as industrial internet of things, bio-informatics, and econometrics due to its ability to deal with limited data, collinearity, and relevance to supervised machine learning. PLS has also been applied to system identification and subspace identification where the model is high-order, high-dimension, or collinear due to the lack of rich excitation. How-ever, all PLS algorithms to date are iterative in calculating the sequence of latent variables, unlike other related methods such as principal component regression. The iterative PLS estimation has made it difficult to perform statistical analysis. In this paper, we propose a novel non-iterative PLS algorithm based on the Krylov sequence used in PLS algorithms. Only a singular value decomposition is needed to obtain an equivalent PLS model for multiple PLS latent factors. The non-iterative PLS algorithm extracts the same latent space as the conventional PLS, which is demonstrated with a couple of industrial application cases.
Original languageEnglish
Title of host publication2021 60th IEEE Conference on Decision and Control (CDC)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781665436595, 9781665436588
ISBN (Print)9781665436601
Publication statusPublished - Dec 2021
Externally publishedYes
Event60th IEEE Conference on Decision and Control (CDC 2021) - Fairmont Hotel (Virtual), Austin, United States
Duration: 13 Dec 202117 Dec 2021

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference60th IEEE Conference on Decision and Control (CDC 2021)
Country/TerritoryUnited States

Bibliographical note

Financial support for this work from the Natural Science Foundation of China grant (U20A201398), Big data-driven abnormal situation intelligent diagnosis and self-healing control for process industries, and City University of Hong Kong Project (9380123), SGP: Bridging between Systems Theory and Dynamic Data Learning towards Industrial Intelligence and Industry 4.0, is gratefully acknowledged.


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