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

Authors:
Arthur Tenenhaus;Alain Giron;Emmanuel Viennet;Michel Béra;Gilbert Saporta;Bernard Fertil
Affiliations:
U678 INSERM, CHU Pitié-Salpétrière, 91 bd de l'hôpital, 75634 Paris, France and KXEN research, 25 quai Galliéni, 92150 Suresnes, France;U678 INSERM, CHU Pitié-Salpétrière, 91 bd de l'hôpital, 75634 Paris, France;Laboratoire d'informatique LIPN, Université Paris XIII, France;KXEN research, 25 quai Galliéni, 92150 Suresnes, France;CNAM , 292 rue Saint Martin, case 441, 75141 Paris cedex 03, France;U678 INSERM, CHU Pitié-Salpétrière, 91 bd de l'hôpital, 75634 Paris, France and Laboratoire LSIS (UMR CNRS 6168), Equipe I&M (ESIL), case 925, 163, avenue de Luminy, 13288 Mars ...
Venue:
Computational Statistics & Data Analysis
Year:
2007

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Abstract

''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.