Building a sparse kernel classifier on riemannian manifold

Authors:
Yanyun Qu;Zejian Yuan;Nanning Zheng
Affiliations:
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, P.R. China;Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, P.R. China;Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, P.R. China
Venue:
VSMM'06 Proceedings of the 12th international conference on Interactive Technologies and Sociotechnical Systems
Year:
2006

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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.