title: A new method combining LDA and PLS for dimension reduction.
abstract: 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.
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing, China; Network Information Center, Harbin University of Science and Technology, Harbin, China.
authors.forename: Liang
authors.initials: L
authors.lastname: Tang
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors.forename: Silong
authors.initials: S
authors.lastname: Peng
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors.forename: Yiming
authors.initials: Y
authors.lastname: Bi
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Shan
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors.forename: Xiyuan
authors.initials: X
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D016002:Discriminant Analysis; D016018:Least-Squares Analysis; D013059:Spectrum Analysis, Raman; D013223:Statistics as Topic / Q000379:methods*