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
2107-2113
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
November 2012
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), .
(doi:10.1080/00207721.2011.564673).
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.
Text
IJSS-2012-11.pdf
- Version of Record
Restricted to Repository staff only
Request a copy
More information
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
Catalogue record
Date deposited: 01 Oct 2012 12:57
Last modified: 14 Mar 2024 12:01
Export record
Altmetrics
Contributors
Author:
Xia Hong
Author:
Sheng Chen
Author:
Chris J. Harris
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics