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

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. (c) 2007 Elsevier B.V. All rights reserved.