ON THE ESTIMATION OF A COVARIANCE MATRIX IN DESIGNING PARZEN CLASSIFIERS

摘要

The design of the Parzen classifiers requires careful attention to the window-width as well as kernel covariance matrices. Although a considerable amount of effort has been devoted to the selection of the window-width, the problem of estimating kernel covariance matrices has received little attention in the past. In this paper we discuss the kernel covariance estimators for the design of the Parzen classifiers. We compare the performance of the Parzen classifiers based on several kernel covariance estimators such as the Toeplitz, Ness's and orthogonal expansion estimators on three artificial data sets. From experimental results, we recommend the use of the Toeplitz estimator, particularly in high-dimensional spaces. Copyright (C) 1996 Pattern Recognition Society. [References: 11]