A multi-class classifying algorithm based on nonlinear dimensionality reduction and support vector machines

Authors:
Lukui Shi;Qing Wu;Xueqin Shen;Pilian He
Affiliations:
School of Electronic and Information Engineering, Tianjin University, Tianjin, China;School of Computer Science and Engineering, Hebei University of Technology, Tianjin, China;School of Computer Science and Engineering, Hebei University of Technology, Tianjin, China;School of Electronic and Information Engineering, Tianjin University, Tianjin, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

Many problems in pattern classifications involve some form of dimensionality reduction. ISOMAP is a representative nonlinear dimensionality reduction algorithm, which can discover low dimensional manifolds from high dimensional data. To speed ISOMAP and decrease the dependency to the neighborhood size, we propose an improved algorithm. It can automatically select a proper neighborhood size and an appropriate landmark set according to a stress function. A multi-class classifier with high efficiency is obtained through combining the improved ISOMAP with SVM. Experiments show that the classifier presented is effective in fingerprint classifications.