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Using zero-norm constraint for sparse probability density function estimation

Using zero-norm constraint for sparse probability density function estimation
Using zero-norm constraint for sparse probability density function estimation
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.
cross-validation, parzen window, probability density function, sparse modelling
0020-7721
2107-2113
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Hong, Xia, Chen, Sheng and Harris, Chris J. (2012) Using zero-norm constraint for sparse probability density function estimation. International Journal of Systems Science, 43 (11), 2107-2113. (doi:10.1080/00207721.2011.564673).

Record type: Article

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.

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e-pub ahead of print date: 11 April 2011
Published date: November 2012
Keywords: cross-validation, parzen window, probability density function, sparse modelling
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 343218
URI: http://eprints.soton.ac.uk/id/eprint/343218
ISSN: 0020-7721
PURE UUID: 2312c4ba-70fa-4b72-9f9f-81db6339e6e8

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Date deposited: 01 Oct 2012 12:57
Last modified: 30 Sep 2019 19:06

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