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Pattern Analysis and Applications
Published in: Pattern Analysis and Applications 2/2015

01-05-2015 | Theoretical Advances

Robust sparse kernel density estimation by inducing randomness

Authors: Fei Chen, Huimin Yu, Jincao Yao, Roland Hu

Published in: Pattern Analysis and Applications | Issue 2/2015

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Abstract

In this paper, a robust sparse kernel density estimation based on the reduced set density estimator is proposed. The key idea is to induce randomness to the plug-in estimation of weighting coefficients. The random fluctuations can inhibit these small nonzero weighting coefficients to cluster in regions of space with greater probability mass. By sequential minimal optimization, these coefficients are merged into a few larger weighting coefficients. Experimental studies show that the proposed model is superior to several related methods both in sparsity and accuracy of the estimation. Moreover, the proposed density estimation is extensively validated on novelty detection and binary classification.
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Appendix
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Metadata
Title
Robust sparse kernel density estimation by inducing randomness
Authors
Fei Chen
Huimin Yu
Jincao Yao
Roland Hu
Publication date
01-05-2015
Publisher
Springer London
Published in
Pattern Analysis and Applications / Issue 2/2015
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-013-0330-1

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