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Sparse density estimator with tunable kernels

Hong, X., Chen, S. and Becerra, V. (2016) Sparse density estimator with tunable kernels. Neurocomputing, 173 (3). pp. 1976-1982. ISSN 0925-2312

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To link to this item DOI: 10.1016/j.neucom.2015.08.021

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:65632
Publisher:Elsevier

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