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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Bibliographic Details
Published in:PloS one 2014-05, Vol.9 (5), p.e96944-e96944
Main Authors: Tang, Liang, Peng, Silong, Bi, Yiming, Shan, Peng, Hu, Xiyuan
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automation
Classification
Comparative analysis
Comparative studies
Computer and Information Sciences
Discriminant Analysis
Economic models
International conferences
Least-Squares Analysis
Methods
Optimization techniques
Physical Sciences
Principal components analysis
Reduction
Regression analysis
Spectrum Analysis, Raman
Statistical analysis
Statistics as Topic - methods
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0096944