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Kernel density construction using orthogonal forward regression

Chen, S., Hong, X. ORCID: and Harris, C. J. (2004) Kernel density construction using orthogonal forward regression. Lecture Notes in Computer Science, 3177. pp. 586-592. ISSN 0302-9743 3-540-22881-0

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An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15166
Additional Information:Proceedings Paper 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004) AUG 25-27, 2004 Exeter, ENGLAND

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