CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
A New Method Combining LDA and PLS for Dimension Reduction.
Tang, Liang; Peng, Silong; Bi, Yiming; Shan, Peng; Hu, Xiyuan,
Source PublicationPLos One
2014
Volume9(5)Issue:5Pages:e96944-e96944 (SCI)
AbstractLinear 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.
KeywordModified Split Hopkinson Torsional Bars Shear Localization Microstructural Evolution
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12907
Collection智能制造技术与系统研究中心_多维数据分析
Corresponding AuthorTang, Liang
Recommended Citation
GB/T 7714
Tang, Liang,Peng, Silong,Bi, Yiming,et al. A New Method Combining LDA and PLS for Dimension Reduction.[J]. PLos One,2014,9(5)(5):e96944-e96944 (SCI).
APA Tang, Liang,Peng, Silong,Bi, Yiming,Shan, Peng,&Hu, Xiyuan,.(2014).A New Method Combining LDA and PLS for Dimension Reduction..PLos One,9(5)(5),e96944-e96944 (SCI).
MLA Tang, Liang,et al."A New Method Combining LDA and PLS for Dimension Reduction.".PLos One 9(5).5(2014):e96944-e96944 (SCI).
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