The Kernel Density Estimate (KDE) is a non-parametric density estimate which has broad application in computer vision and pattern recognition. In particular, the Mean Shift procedure uses the KDE structure in order to cluster or segment data, including images and video. The usefulness of these twin techniques – KDEs and Mean Shift – on large datasets is hampered by the large space or descriptio...

We propose a novel approach to online estimation of probability density functions, which is based on kernel density estimation (KDE). The method maintains and updates a non-parametric model of the observed data, from which the KDE can be calculated. We propose an online bandwidth estimation approach and a compression/revitalization scheme which maintains the KDE’s complexity low. We compare the...

Accurately predicting the cardinality of intermediate plan operations is an essential part of any modern relational query optimizer. The accuracy of said estimates has a strong and direct impact on the quality of the generated plans, and incorrect estimates can have a negative impact on query performance. One of the biggest challenges in this field is to predict the result size of join operatio...

Human light chain genes are used in a kappa before lambda order. Accompanying this hierarchy is the rearrangement of a kappa-deleting element (Kde) which eliminates the kappa locus before lambda gene rearrangement. In approximately 60% of rearrangements the Kde recombines at a conserved heptamer within the J kappa-C kappa intron. We demonstrated that aberrant V/J rearrangements possessing appar...

In this paper we introduce some tests for the comparison of k samples based on kernel density estimators (KDE), and we develope the Double Minimum method as a new and useful procedure for the crucial problem of bandwidth selection. We study, via Monte Carlo simulations, the statistical power of the proposed tests, as well as the impact of the smoothing degree and the performance of the Double M...

Kernel density estimation (KDE) has been used in many computational intelligence and computer vision applications. In this paper we propose a Bayesian estimation method for finding the bandwidth in KDE applications. A Gamma density function is fitted to distributions of variances of K-nearest neighbours data populations while uniform distribution priors are assumed for K. A maximum log-likeliho...

The number of modes (also known as modality) of a kernel density estimator (KDE) draws lots of interests and is important in practice. In this paper, we develop an inference framework on the modality of a KDE under multivariate setting using Gaussian kernel. We applied the modal clustering method proposed by [1] for mode hunting. A test statistic and its asymptotic distribution are derived to a...

While robust parameter estimation has been well studied in parametric density estimation, there has been little investigation into robust density estimation in the nonparametric setting. We present a robust version of the popular kernel density estimator (KDE). As with other estimators, a robust version of the KDE is useful since sample contamination is a common issue with datasets. What “robus...

data types geospatial data modeling, database perspective. Voisard, A., + , T-KDE Mar-Apr 02 226-243 Abstract data types; cf. Inheritance Active databases act. database trigger condition testing and view maint. using optimized discrim. networks. Hanson, E.N., + , T-KDE Mar-Apr 02 261-280 real-time act. database systs. concurrency control. Datta, A., + , T-KDE May-Jun 02 465-484 Administrative d...