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Sparse density estimator with tunable kernels

Sparse density estimator with tunable kernels
Sparse density estimator with tunable kernels
A new sparse kernel density estimator with tunable kernels is introduced within a forward constrained regression framework whereby the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Based on the minimum integrated square error criterion, a recursive algorithm is developed to select significant kernels one at time, and the kernel width of the selected kernel is then tuned using the gradient descent algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing very sparse kernel density estimators with competitive accuracy to existing kernel density estimators.
probability density function, kernel density estimator, sparse modeling, minimum integrated square error
0925-2312
1976-1982
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Becerra, Victor M.
953ddde2-d86e-4805-9f44-d71ff93edd53
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Becerra, Victor M.
953ddde2-d86e-4805-9f44-d71ff93edd53

Hong, Xia, Chen, Sheng and Becerra, Victor M. (2016) Sparse density estimator with tunable kernels. Neurocomputing, 173, 1976-1982. (doi:10.1016/j.neucom.2015.08.021).

Record type: Article

Abstract

A new sparse kernel density estimator with tunable kernels is introduced within a forward constrained regression framework whereby the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Based on the minimum integrated square error criterion, a recursive algorithm is developed to select significant kernels one at time, and the kernel width of the selected kernel is then tuned using the gradient descent algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing very sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

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More information

Accepted/In Press date: 3 August 2015
e-pub ahead of print date: 18 August 2015
Published date: 15 January 2016
Keywords: probability density function, kernel density estimator, sparse modeling, minimum integrated square error
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 383075
URI: http://eprints.soton.ac.uk/id/eprint/383075
ISSN: 0925-2312
PURE UUID: 68c28c76-355e-4d22-b71f-68a6773d91bc

Catalogue record

Date deposited: 21 Oct 2015 13:52
Last modified: 16 Dec 2019 20:13

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