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Article Dans Une Revue Computational Statistics and Data Analysis Année : 2007

Kernel logistic PLS: a tool for supervised nonlinear dimensionality reduction and binary classification

Résumé

Kernel logistic PLS" (KL-PLS) is a new tool for supervised nonlinear dimensionality reduction and binary classification. The principles of KL-PLS are based on both PLS latent variables construction and learning with kernels. The KL-PLS algorithm can be seen as a supervised dimensionality reduction (complexity control step) followed by a classification based on logistic regression. The algorithm is applied to 11 benchmark data sets for binary classification and to three medical problems. In all cases, KL-PLS proved its competitiveness with other state-of-the-art classification methods such as support vector machines. Moreover, due to successions of regressions and logistic regressions carried out on only a small number of uncorrelated variables, KL-PLS allows handling high-dimensional data. The proposed approach is simple and easy to implement. It provides an efficient complexity control by dimensionality reduction and allows the visual inspection of data segmentation.
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hal-00152898 , version 1 (02-03-2021)

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Arthur Tenenhaus, Alain Giron, Emmanuel Viennet, Michel Béra, Gilbert Saporta, et al.. Kernel logistic PLS: a tool for supervised nonlinear dimensionality reduction and binary classification. Computational Statistics and Data Analysis, 2007, 51 (9), pp.4083-4100. ⟨10.1016/j.csda.2007.01.004⟩. ⟨hal-00152898⟩
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