Article ID Journal Published Year Pages File Type
530362 Pattern Recognition 2011 11 Pages PDF
Abstract

A conditional density function, which describes the relationship between response and explanatory variables, plays an important role in many analysis problems. In this paper, we propose a new kernel-based parametric method to estimate conditional density. An exponential function is employed to approximate the unknown density, and its parameters are computed from the given explanatory variable via a nonlinear mapping using kernel principal component analysis (KPCA). We develop a new kernel function, which is a variant to polynomial kernels, to be used in KPCA. The proposed method is compared with the Nadaraya–Watson estimator through numerical simulation and practical data. Experimental results show that the proposed method outperforms the Nadaraya–Watson estimator in terms of revised mean integrated squared error (RMISE). Therefore, the proposed method is an effective method for estimating the conditional densities.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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