Using zero-norm constraint for sparse probability density function estimation

Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298, Chen, S. and Harris, C. (2012) Using zero-norm constraint for sparse probability density function estimation. International Journal of Systems Science, 43 (11). pp. 2107-2113. ISSN 0020-7721 doi: 10.1080/00207721.2011.564673

Abstract/Summary

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.