Discriminant subspace learning based on support vectors machines

Authors:
Nikolaos Pitelis;Anastasios Tefas
Affiliations:
School of Electronic Engineering and Computer Science, Queen Mary, University of London, UK;Department of Informatics, Aristotle University of Thessaloniki, Greece
Venue:
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2012

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Abstract

A new method for dimensionality reduction and feature extraction based on Support Vector Machines and minimization of the within-class data dispersion is proposed. An iterative procedure is proposed that successively applies Support Vector Machines on perpendicular subspaces using the deflation transformation in such a way that the within-class variance is minimized. The proposed approach is proved to be a successive SVM using deflation kernels. The normal vectors of the successive hyperplanes contain discriminant information and they can be used as projection vectors for feature extraction and dimensionality reduction of the data. Experiments on various datasets are conducted in order to highlight the superior performance of the proposed algorithm.