ParzenWindows Estimation Using Laplace Kernel: A Novel Parametric Analysis with Information Content

Parzen windows estimation is one of the classical non- parametric methods in the field of machine learning and pattern classification, and usually uses Gaussian density function as the kernel. Although the relation between the kernel density estimation (KDE) and low-pass filtering is well known, it is vary difficult to setting the parameters of the other kinds of density functions. This paper proposes a novel method to deal with the parameters of Laplace kernel through measuring the degree of exchanged information among interpolating points. Experimental results showed that the proposed method can improve the performance of Parzen windows significantly.