Sparse density estimator with tunable kernels

NEUROCOMPUTING

卷:173

Article

Hong, Xia¹  Chen, Sheng^2,3  Becerra, Victor M.¹ 

[1] Univ Reading, Sch Syst Engn, Reading RG6 6AY, Berks, England

[2] Univ Southampton, Elect & Comp Sci, Southampton SO17 1BJ, Hants, England

[3] King Abdulaziz Univ, Jeddah 27589, Saudi Arabia

关键词: Probability density function;    Kernel density estimator;    Sparse modeling;    Minimum integrated square error;   

DOI  :  10.1016/j.neucom.2015.08.021

来源: Elsevier

【 摘 要 】

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. (C) 2015 Published by Elsevier B.V.

【 授权许可】

Free