Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps-Reference-Cited by-同舟云学术

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

Published:2018 Issue:15 Volume:143 Page:3526-3539
ISSN:0003-2654
Container-title:The Analyst
language:en
Short-container-title:Analyst

Author:

Lee Loong Chuen¹²³⁴⁵^ORCID,Liong Choong-Yeun⁶⁷³⁸⁵^ORCID,Jemain Abdul Aziz⁶⁷³⁸⁵^ORCID

Affiliation:

1. Forensic Science Programme

2. FSK

3. Universiti Kebangsaan Malaysia

4. 50300 Kuala Lumpur

5. Malaysia

6. Statistics Programme

7. FST

8. 43600 Bangi

Abstract

This review highlights and discusses critically various knowledge gaps in classification modelling using PLS-DA for high dimensional data.

Funder

Universiti Kebangsaan Malaysia

Ministry of Higher Education, Malaysia

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrochemistry,Spectroscopy,Environmental Chemistry,Biochemistry,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2018/AN/C8AN00599K

Reference159 articles.

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3. M. L. Barker , Partial least squares for discrimination, statistical theory and implementation , LAP LAMBERT Academic Publishing , Germany , 2015

4. Classification tools in chemistry. Part 1: linear models. PLS-DA

5. The diversity in the applications of partial least squares: an overview

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