Kernel density estimation (KDE) is a widely used method in geography to study concentration of point pattern data. Geographical networks are 1.5 dimensional spaces with specific characteristics, analyzing events occurring on (accidents roads, leakages pipes, species along rivers, etc.). In the last decade, they required extension spatial KDE. Several versions Network KDE (NKDE) have been propos...

This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Densitybased Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a local KDE method based on extended nearest neighbors of the object. Instead of...

With the adoption of Nepomuk as an organic part of KDE the semantic desktop became a reality to a great number of users and is employed by a growing number of applications. Thus, the amount of semantic data is constantly growing on the desktop. Therefore users need a way to access this data outside of the limiting use cases of the applications employing Nepomuk-KDE. We aim to assist users in bu...

We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE) to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided...

Modal regression estimates the local modes of the distribution of Y given X = x, instead of the mean, as in the usual regression sense, and can hence reveal important structure missed by usual regression methods. We study a simple nonparametric method for modal regression, based on a kernel density estimate (KDE) of the joint distribution of Y and X. We derive asymptotic error bounds for this m...

We present results of experiments testing the Fast Gauss Transform, Improved Fast Gauss Transform, and Dual-Tree methods (using kd-tree and Anchors Hierarchy data structures) for fast Kernel Density Estimation (KDE). We examine the performance of these methods with respect to data set size, dimension, allowable error, and data set structure (“clumpiness”), measured in terms of CPU time and memo...

Moving object detection based on monitoring video system is often a challenging problem. Specially to monitor traffic at both day and night, in different weather and illumination conditions and with changeable background. Kernel Density Estimation (KDE) model is an effective approach to judge background and foreground, however, typical KDE uses fixed parameters, such as bandwidths, threshold, e...

The Markov Chain Monte Carlo approach is frequently used within a Bayesian framework to sample the target posterior distribution. Its efficiency strongly depends on proposal distribution build chain. best jump one that closely resembles unknown distribution; therefore, we suggest an adaptive based kernel density estimation (KDE). We group model's parameters according their correlation and KDE a...