Building a Sparse Kernel Classifier on Riemannian Manifold

  • Yanyun Qu
  • Zejian Yuan
  • Nanning Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4270)


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.


Support Vector Machine Support Vector Riemannian Manifold Relevance Vector Machine Machine Learn Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yanyun Qu
    • 1
    • 2
  • Zejian Yuan
    • 1
  • Nanning Zheng
    • 1
  1. 1.Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’anP.R. China
  2. 2.Computer Science DepartmentXiamen UniversityXiamenP.R. China

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