Investigation on Sparse Kernel Density Estimator Via Harmony Data Smoothing Learning

Authors:
Xuelei Hu;Yingyu Yang
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

In this paper we apply harmony data smoothing learning on a weighted kernel density model to obtain a sparse density estimator. We empirically compare this method with the least squares cross-validation (LSCV) method for the classical kernel density estimator. The most remarkable result of our study is that the harmony data smoothing learning method outperforms LSCV method in most cases and the support vectors selected by harmony data smoothing learning method are located in the regions of local highest density of the sample.