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Using zero-norm constraint for sparse probability density function estimation

Author

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  • X. Hong
  • S. Chen
  • C.J. Harris

Abstract

A new sparse kernel probability density function (pdf) estimator based on zero-norm constraint is constructed using the classical Parzen window (PW) estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

Suggested Citation

  • X. Hong & S. Chen & C.J. Harris, 2012. "Using zero-norm constraint for sparse probability density function estimation," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(11), pages 2107-2113.
  • Handle: RePEc:taf:tsysxx:v:43:y:2012:i:11:p:2107-2113
    DOI: 10.1080/00207721.2011.564673
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    Cited by:

    1. Yan Aijun & Huang Xiaoqian & Shao Hongshan, 2015. "On the Sparseness and Generalization Capability of Least Squares Support Vector Machines," Journal of Systems Science and Information, De Gruyter, vol. 3(3), pages 279-288, June.

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