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Sparse kernel density estimation technique based on zero-norm constraint

Sparse kernel density estimation technique based on zero-norm constraint
Sparse kernel density estimation technique based on zero-norm constraint
A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity. The classical Parzen window estimate is adopted as the desired response for density estimation, and an approximate function of the zero-norm is used for achieving mathemtical tractability and algorithmic efficiency. Under the mild condition of the positive definite design matrix, the kernel weights of the proposed density estimator based on the zero-norm approximation can be obtained using the multiplicative nonnegative quadratic programming algorithm. Using the $D$-optimality based selection algorithm as the preprocessing to select a small significant subset design matrix, the proposed zero-norm based approach offers an effective means for constructing very sparse kernel density estimates with excellent generalisation performance.
3782-3787
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. (2010) Sparse kernel density estimation technique based on zero-norm constraint. At IJCNN 2010 IJCNN 2010, Spain. 18 - 23 Jul 2010. pp. 3782-3787.

Record type: Conference or Workshop Item (Other)

Abstract

A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity. The classical Parzen window estimate is adopted as the desired response for density estimation, and an approximate function of the zero-norm is used for achieving mathemtical tractability and algorithmic efficiency. Under the mild condition of the positive definite design matrix, the kernel weights of the proposed density estimator based on the zero-norm approximation can be obtained using the multiplicative nonnegative quadratic programming algorithm. Using the $D$-optimality based selection algorithm as the preprocessing to select a small significant subset design matrix, the proposed zero-norm based approach offers an effective means for constructing very sparse kernel density estimates with excellent generalisation performance.

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More information

Published date: July 2010
Additional Information: Event Dates: July 18-23, 2010
Venue - Dates: IJCNN 2010, Spain, 2010-07-18 - 2010-07-23
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 271407
URI: https://eprints.soton.ac.uk/id/eprint/271407
PURE UUID: 533a2f79-2b81-4dc6-9ffa-1f6ac36474ba

Catalogue record

Date deposited: 15 Jul 2010 14:48
Last modified: 18 Jul 2017 06:43

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Contributors

Author: Xia Hong
Author: Sheng Chen
Author: Chris J. Harris

University divisions

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