Combining local and global smoothing in multivariate density estimation
Non-parametric estimation of a multivariate density estimation is tackled via a method which combines traditional local smoothing with a form of global smoothing but without imposing a rigid structure. Simulation work delivers encouraging indications on the effectiveness of the method. An application to density-based clustering illustrates a possible usage.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringDensity EstimationSimilar Papers 제목 키워드 기반
Multivariate Smoothing via the Fourier Integral Theorem and Fourier Kernel
Starting with the Fourier integral theorem, we present natural Monte Carlo estimators of multivariate functions including densities, mixing densities, transition densities, regression functions, and the search for modes …
regressionDual Mamba for Node-Specific Representation Learning: Tackling Over-Smoothing with Selective State Space Modeling
Over-smoothing remains a fundamental challenge in deep Graph Neural Networks (GNNs), where repeated message passing causes node representations to become indistinguishable. While existing solutions, such as residual conn…
Representation LearningMultivariate root-n-consistent smoothing parameter free matching estimators and estimators of inverse density weighted expectations
Expected values weighted by the inverse of a multivariate density or, equivalently, Lebesgue integrals of regression functions with multivariate regressors occur in various areas of applications, including estimating ave…
regressionUnsupervised anomaly detection algorithms on real-world data: how many do we need?
In this study we evaluate 32 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular datasets, performing the largest comparison of unsupervised anomaly detection algorithms to date. On this colle…
Anomaly DetectionUnsupervised Anomaly DetectionA Simple And Effective Filtering Scheme For Improving Neural Fields
Recently, neural fields, also known as coordinate-based MLPs, have achieved impressive results in representing low-dimensional data. Unlike CNN, MLPs are globally connected and lack local control; adjusting a local regio…
Surface Reconstruction