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Constructing Sparse Kernel Machines Using Attractors




In this brief, a novel method that constructs a sparse kernel machine is proposed. The proposed method generates attractors as sparse solutions from a built-in kernel machine via a dynamical system framework. By readjusting the corresponding coefficients and bias terms, a sparse kernel machine that approximates a conventional kernel machine is constructed. The simulation results show that the constructed sparse kernel machine improves the efficiency of testing phase while maintaining comparable test error.

Author(s): Lee, D. and Jung, K-H. and Lee, J.
Journal: IEEE Transactions on Neural Networks
Volume: 20
Number (issue): 4
Pages: 721-729
Year: 2009
Month: April
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1109/TNN.2009.2014059
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Constructing Sparse Kernel Machines Using Attractors},
  author = {Lee, D. and Jung, K-H. and Lee, J.},
  journal = {IEEE Transactions on Neural Networks},
  volume = {20},
  number = {4},
  pages = {721-729},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = apr,
  year = {2009},
  month_numeric = {4}