A New Method Combining LDA and PLS for Dimension Reduction
Tang, Liang1,2; Peng, Silong1; Bi, Yiming1; Shan, Peng1; Hu, Xiyuan1
发表期刊PLOS ONE
2014-05-12
卷号9期号:5页码:e96944
文章类型Article
摘要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.
WOS标题词Science & Technology
关键词[WOS]PARTIAL LEAST-SQUARES ; GENERALIZED LINEAR-REGRESSION ; PRINCIPAL COMPONENT ANALYSIS ; DISCRIMINANT-ANALYSIS ; CLASSIFIER ENSEMBLES ; ALGORITHM ; EFFICIENT ; OILS
收录类别SCI
语种英语
WOS研究方向Science & Technology - Other Topics
WOS类目Multidisciplinary Sciences
WOS记录号WOS:000336653300053
引用统计
被引频次:31[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/2905
专题智能制造技术与系统研究中心_多维数据分析(彭思龙)-技术团队
作者单位1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Harbin Univ Sci & Technol, Network Informat Ctr, Harbin, Peoples R China
第一作者单位中国科学院自动化研究所
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Tang, Liang,Peng, Silong,Bi, Yiming,et al. A New Method Combining LDA and PLS for Dimension Reduction[J]. PLOS ONE,2014,9(5):e96944.
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),e96944.
MLA Tang, Liang,et al."A New Method Combining LDA and PLS for Dimension Reduction".PLOS ONE 9.5(2014):e96944.
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