Using method of Nadaraya-Watson kernel regression to detection outliers in multivariate data fusion

In this paper, the researcher discussed a developed approach to the detection of outliers that is suited to multivariate data fusion. The challenge in outlier detection when dealing with multivariate data it is the detection of the outlier with more than two dimensions. To address this issue, the researcher developed a method to detect anomalies using methods based on local density including comparing a specific observations density with the densities of its neighboring observations. To make such comparisons, the researcher often employs an outlier score. In this study, various density estimation functions and distance metrics were utilized. Nadaraya-Watson kernel regression for multivariate data considered the KNN with multivariate data. Finally, the estimate of the Volcano kernel method is an essential method for outliers detection. In the simulation experiments of multivariate data with (4,6,8) variables and (60,120,180) observations, the results of simulation experiments by using the criterion of the precision evaluation showed that the N-W method is better than the VOL method in outlier detection in multivariate data.