Neural Computing and Applications

, Volume 20, Issue 5, pp 703–715 | Cite as

An unsupervised approach to learn the kernel functions: from global influence to local similarity

  • M. Ehsan Abbasnejad
  • Dhanesh Ramachandram
  • Rajeswari Mandava
Original Article


Recently there has been a steep growth in the development of kernel-based learning algorithms. The intrinsic problem in such algorithms is the selection of the optimal kernel for the learning task of interest. In this paper, we propose an unsupervised approach to learn a linear combination of kernel functions, such that the resulting kernel best serves the objectives of the learning task. This is achieved through measuring the influence of each point on the structure of the dataset. This measure is calculated by constructing a weighted graph on which a random walk is performed. The measure of influence in the feature space is probabilistically related to the input space that yields an optimization problem to be solved. The optimization problem is formulated in two different convex settings, namely linear and semidefinite programming, dependent on the type of kernel combination considered. The contributions of this paper are twofold: first, a novel unsupervised approach to learn the kernel function, and second, a method to infer the local similarity represented by the kernel function by measuring the global influence of each point toward the structure of the dataset. The proposed approach focuses on the kernel selection which is independent of the kernel-based learning algorithm. The empirical evaluation of the proposed approach with various datasets shows the effectiveness of the algorithm in practice.


Learning the kernels Kernel methods Support vector machine Kernel-PCA 



This research has been made possible through the Science Fund Grant “Delineation and 3D Visualization of Tumor and Risk Structures” (DVTRS), No: 1001/PKOMP/817001 by the Ministry of Science, Technology and Innovation of Malaysia.


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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • M. Ehsan Abbasnejad
    • 1
  • Dhanesh Ramachandram
    • 1
  • Rajeswari Mandava
    • 1
  1. 1.Computer Vision Research Group, School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia

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