Search from over 60,000 research works

Advanced Search

Sparse kernel density estimation technique based on zero-norm constraint

[thumbnail of ijcnn2010-2.pdf]
Preview
ijcnn2010-2.pdf - Accepted Version (155kB) | Preview
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Hong, X. orcid id iconORCID: https://orcid.org/0000-0002-6832-2298, Chen, S. and Harris, C. J. (2010) Sparse kernel density estimation technique based on zero-norm constraint. In: The 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 3782-3787. ISBN 9781424469161 doi: 10.1109/IJCNN.2010.5596853

Abstract/Summary

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 -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.

Altmetric Badge

Additional Information The conference was held in Barcelona, Spain, 18-23 July 2010.
Item Type Book or Report Section
URI https://reading-clone.eprints-hosting.org/id/eprint/16724
Item Type Book or Report Section
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Additional Information The conference was held in Barcelona, Spain, 18-23 July 2010.
Publisher IEEE
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar