Girolami, M. and Chao, H. (2003) Probability density estimation from optimally condensed data samples. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), pp. 1253-1264. (doi: 10.1109/TPAMI.2003.1233899)
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probability_density_estimation.pdf 2MB |
Publisher's URL: http://dx.doi.org/10.1109/TPAMI.2003.1233899
Abstract
The requirement to reduce the computational cost of evaluating a point probability density estimate when employing a Parzen window estimator is a well-known problem. This paper presents the Reduced Set Density Estimator that provides a kernel-based density estimator which employs a small percentage of the available data sample and is optimal in the L/sub 2/ sense. While only requiring /spl Oscr/(N/sup 2/) optimization routines to estimate the required kernel weighting coefficients, the proposed method provides similar levels of performance accuracy and sparseness of representation as Support Vector Machine density estimation, which requires /spl Oscr/(N/sup 3/) optimization routines, and which has previously been shown to consistently outperform Gaussian Mixture Models. It is also demonstrated that the proposed density estimator consistently provides superior density estimates for similar levels of data reduction to that provided by the recently proposed Density-Based Multiscale Data Condensation algorithm and, in addition, has comparable computational scaling. The additional advantage of the proposed method is that no extra free parameters are introduced such as regularization, bin width, or condensation ratios, making this method a very simple and straightforward approach to providing a reduced set density estimator with comparable accuracy to that of the full sample Parzen density estimator.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Girolami, Prof Mark |
Authors: | Girolami, M., and Chao, H. |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
College/School: | College of Science and Engineering > School of Computing Science |
Journal Name: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Publisher: | Institute of Electrical and Electronics Engineers |
ISSN: | 0162-8828 |
Copyright Holders: | Copyright © 2003 Institute of Electrical and Electronics Engineers |
First Published: | First published in IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10):1253-1264 |
Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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