Abstract
It is difficult to deal with large datasets by kernel based methods since the number of basis functions required for an optimal solution equals the number of samples. We present an approach to build a sparse kernel classifier by adding constraints to the number of support vectors and to the classifier function. The classifier is considered on Riemannian manifold. And the sparse greedy learning algorithm is used to solve the formulated problem. Experimental results over several classification benchmarks show that the proposed approach can reduce the training and runtime complexities of kernel classifier applied to large datasets without scarifying high classification accuracy.
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© 2006 Springer-Verlag Berlin Heidelberg
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Qu, Y., Yuan, Z., Zheng, N. (2006). Building a Sparse Kernel Classifier on Riemannian Manifold. In: Zha, H., Pan, Z., Thwaites, H., Addison, A.C., Forte, M. (eds) Interactive Technologies and Sociotechnical Systems. VSMM 2006. Lecture Notes in Computer Science, vol 4270. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11890881_18
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DOI: https://doi.org/10.1007/11890881_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46304-7
Online ISBN: 978-3-540-46305-4
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