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Kernel density construction using orthogonal forward regression

Chen, S., Hong, X. 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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Abstract/Summary

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
Refereed:Yes
Divisions:Faculty of 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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