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A fast algorithm for sparse probability density function construction

A fast algorithm for sparse probability density function construction
A fast algorithm for sparse probability density function construction
A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Hong, Xia and Chen, Sheng (2013) A fast algorithm for sparse probability density function construction. 18th International Conference on Signal Processing, Greece. 01 - 03 Jul 2013. 6 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators

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Published date: July 2013
Venue - Dates: 18th International Conference on Signal Processing, Greece, 2013-07-01 - 2013-07-03
Organisations: Southampton Wireless Group

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Local EPrints ID: 354095
URI: http://eprints.soton.ac.uk/id/eprint/354095
PURE UUID: 57a34c9b-39a1-4b30-9dcd-c3e8bd65caf8

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Date deposited: 01 Jul 2013 10:54
Last modified: 19 Jul 2019 21:33

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