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.
This work was supported by Natural Science Foundation of Hebei province of China (No. 603037 and No. E2005000024) and supported by Science-Technology Development Project of Tianjin of China (No. 04310941R).
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© 2005 Springer-Verlag Berlin Heidelberg
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Shi, L., Wu, Q., Shen, X., He, P. (2005). A Multi-class Classifying Algorithm Based on Nonlinear Dimensionality Reduction and Support Vector Machines. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_90
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DOI: https://doi.org/10.1007/11539087_90
Publisher Name: Springer, Berlin, Heidelberg
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