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An orthogonal forward regression technique for sparse kernel density estimation

Chen, S., Hong, X. and Harris, C. J. (2008) An orthogonal forward regression technique for sparse kernel density estimation. Neurocomputing, 71 (4-6). pp. 931-943. ISSN 0925-2312

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To link to this item DOI: 10.1016/j.neucom.2007.02.008


Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights of the selected sparse model are finally updated using the multiplicative nonnegative quadratic programming algorithm, which ensures the nonnegative and unity constraints for the kernel weights and has the desired ability to reduce the model size further. Except for the kernel width, the proposed method has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Several examples demonstrate the ability of this simple regression-based approach to effectively construct a SKID estimate with comparable accuracy to that of the full-sample optimised PW density estimate. (c) 2007 Elsevier B.V. All rights reserved.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15169
Uncontrolled Keywords:probability density function, Parzen window estimate, sparse kernel, modelling, orthogonal forward regression, cross validation, regularisation, multiplicative nonnegative quadratic programming, LEAST-SQUARES, LOCAL REGULARIZATION, IDENTIFICATION

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