Filippone, M. and Sanguinetti, G. (2011) Approximate inference of the bandwidth in multivariate kernel density estimation. Computational Statistics and Data Analysis, 55(12), pp. 3104-3122. (doi: 10.1016/j.csda.2011.05.023)
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Abstract
Kernel density estimation is a popular and widely used non-parametric method for data-driven density estimation. Its appeal lies in its simplicity and ease of implementation, as well as its strong asymptotic results regarding its convergence to the true data distribution. However, a major difficulty is the setting of the bandwidth, particularly in high dimensions and with limited amount of data. An approximate Bayesian method is proposed, based on the Expectation–Propagation algorithm with a likelihood obtained from a leave-one-out cross validation approach. The proposed method yields an iterative procedure to approximate the posterior distribution of the inverse bandwidth. The approximate posterior can be used to estimate the model evidence for selecting the structure of the bandwidth and approach online learning. Extensive experimental validation shows that the proposed method is competitive in terms of performance with state-of-the-art plug-in methods.
Item Type: | Articles |
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Additional Information: | NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics and Data Analysis 55(12), 2011, DOI: 10.1016/j.csda.2011.05.023 |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Filippone, Dr Maurizio |
Authors: | Filippone, M., and Sanguinetti, G. |
College/School: | College of Science and Engineering > School of Computing Science |
Journal Name: | Computational Statistics and Data Analysis |
Publisher: | Elsevier |
ISSN: | 0167-9473 |
ISSN (Online): | 1872-7352 |
Published Online: | 15 June 2011 |
Copyright Holders: | Copyright © 2011 Elsevier |
First Published: | First published in Computational Statistics and Data Analysis 55(12):3104-3122 |
Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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