Sparse probability density function estimation using the minimum integrated square error

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Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298, Chen, S., Qatawneh, A., Daqrouq, K., Sheikh, M. and Morfeq, A. (2013) Sparse probability density function estimation using the minimum integrated square error. Neurocomputing, 114. pp. 122-129. ISSN 0925-2312 doi: 10.1016/j.neucom.2013.02.003

Abstract/Summary

We develop a new sparse kernel density estimator using a forward constrained regression framework, within which the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Our main contribution is to derive a recursive algorithm to select significant kernels one at time based on the minimum integrated square error (MISE) criterion for both the selection of kernels and the estimation of mixing weights. The proposed approach is simple to implement and the associated computational cost is very low. Specifically, the complexity of our algorithm is in the order of the number of training data N, which is much lower than the order of N2 offered by the best existing sparse kernel density estimators. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to those of the classical Parzen window estimate and other existing sparse kernel density estimators.

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Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/32298
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	Probability density function; Sparse modelling; Minimum integrated square error; Forward constrained regression
Publisher	Elsevier
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Date Deposited:	07 May 2013 08:22	Date item deposited into CentAUR
Last Modified:	23 Jun 2024 04:13	Date item last modified

References

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