A sparse kernel density estimation algorithm using forward constrained regression

Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298, Chen, S. and Harris, C. (2007) A sparse kernel density estimation algorithm using forward constrained regression. In: Huang, D. S., Heutte, L. and Loog, M. (eds.) Advanced Intelligent Computing Theories and Applications - with Aspects of Contemporary Intelligent Computing Techniques. Communications in Computer and Information Science, 2. Springer-Verlag Berlin, pp. 1354-1363. ISBN 9783540742814

Abstract/Summary

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.