A new method combining LDA and PLS for dimension reduction.

Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction and classification. In many cases, the projection direction of the classical and extended LDA methods is not considered optimal for special applications. Herein we combine the Partial Least Squares (P...

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Autores principales:	Liang Tang, Silong Peng, Yiming Bi, Peng Shan, Xiyuan Hu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/da1f0ba2699846309ca5312911630bc9
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spelling	oai:doaj.org-article:da1f0ba2699846309ca5312911630bc92021-11-18T08:19:42ZA new method combining LDA and PLS for dimension reduction.1932-620310.1371/journal.pone.0096944https://doaj.org/article/da1f0ba2699846309ca5312911630bc92014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24820185/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction and classification. In many cases, the projection direction of the classical and extended LDA methods is not considered optimal for special applications. Herein we combine the Partial Least Squares (PLS) method with LDA algorithm, and then propose two improved methods, named LDA-PLS and ex-LDA-PLS, respectively. The LDA-PLS amends the projection direction of LDA by using the information of PLS, while ex-LDA-PLS is an extension of LDA-PLS by combining the result of LDA-PLS and LDA, making the result closer to the optimal direction by an adjusting parameter. Comparative studies are provided between the proposed methods and other traditional dimension reduction methods such as Principal component analysis (PCA), LDA and PLS-LDA on two data sets. Experimental results show that the proposed method can achieve better classification performance.Liang TangSilong PengYiming BiPeng ShanXiyuan HuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 5, p e96944 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Liang Tang Silong Peng Yiming Bi Peng Shan Xiyuan Hu A new method combining LDA and PLS for dimension reduction.
description	Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction and classification. In many cases, the projection direction of the classical and extended LDA methods is not considered optimal for special applications. Herein we combine the Partial Least Squares (PLS) method with LDA algorithm, and then propose two improved methods, named LDA-PLS and ex-LDA-PLS, respectively. The LDA-PLS amends the projection direction of LDA by using the information of PLS, while ex-LDA-PLS is an extension of LDA-PLS by combining the result of LDA-PLS and LDA, making the result closer to the optimal direction by an adjusting parameter. Comparative studies are provided between the proposed methods and other traditional dimension reduction methods such as Principal component analysis (PCA), LDA and PLS-LDA on two data sets. Experimental results show that the proposed method can achieve better classification performance.
format	article
author	Liang Tang Silong Peng Yiming Bi Peng Shan Xiyuan Hu
author_facet	Liang Tang Silong Peng Yiming Bi Peng Shan Xiyuan Hu
author_sort	Liang Tang
title	A new method combining LDA and PLS for dimension reduction.
title_short	A new method combining LDA and PLS for dimension reduction.
title_full	A new method combining LDA and PLS for dimension reduction.
title_fullStr	A new method combining LDA and PLS for dimension reduction.
title_full_unstemmed	A new method combining LDA and PLS for dimension reduction.
title_sort	new method combining lda and pls for dimension reduction.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/da1f0ba2699846309ca5312911630bc9
work_keys_str_mv	AT liangtang anewmethodcombiningldaandplsfordimensionreduction AT silongpeng anewmethodcombiningldaandplsfordimensionreduction AT yimingbi anewmethodcombiningldaandplsfordimensionreduction AT pengshan anewmethodcombiningldaandplsfordimensionreduction AT xiyuanhu anewmethodcombiningldaandplsfordimensionreduction AT liangtang newmethodcombiningldaandplsfordimensionreduction AT silongpeng newmethodcombiningldaandplsfordimensionreduction AT yimingbi newmethodcombiningldaandplsfordimensionreduction AT pengshan newmethodcombiningldaandplsfordimensionreduction AT xiyuanhu newmethodcombiningldaandplsfordimensionreduction
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A new method combining LDA and PLS for dimension reduction.

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