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The diversity in the applications of partial least squares: an overview
Author(s) -
Mehmood Tahir,
Ahmed Bilal
Publication year - 2016
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2762
Subject(s) - partial least squares regression , computer science , chemometrics , field (mathematics) , variety (cybernetics) , multivariate statistics , process (computing) , data science , feature selection , software , data mining , variable (mathematics) , machine learning , artificial intelligence , management science , engineering , mathematics , mathematical analysis , pure mathematics , programming language , operating system
Advances in technology make it happen to have massive amount of information in the form of multiple variables per object. The use of multivariate approaches for modeling the real‐life phenomena is natural in such situation. There are numerous multivariate approaches in the literature, and its a challenge to stay updated on the possibilities. Partial least squares (PLSs) are one of the many modeling approaches for high‐throughput data, and its use in different fields to address the variety of problems has been increased in recent years. We therefore present an overview of PLS's applications. The objective of this paper is to give a comprehensive overview on the advances in PLS algorithm together with its applications for regression, classification, variable selection, and survival analysis problems covering genomics, chemometrics, neuroinformatics, process control, computer vision, econometric, environmental studies, and so on. We have mainly presented different PLS approaches and their applications, so that the reader can easily get an understanding of possibility to use PLS for their own field. For further reading, literature references together with software availability are provided. Copyright © 2015 John Wiley & Sons, Ltd.