Nov 20, 2018 · In this paper, we propose a new kernel function to estimate samples' local densities and propose a weighted neighborhood density estimation to increase the ...
We apply our general anomaly detection method to image saliency detection by regarding salient pixels in objects as anomalies to the background regions. Local ...
A new kernel function is proposed to estimate samples' local densities and a weighted neighborhood density estimation to increase the robustness to changes ...
Abstract: Current local density-based anomaly detection methods are limited in that the local density estimation and the neighborhood density estimation are ...
Anomaly detection using local kernel density estimation and context-based regression. Authors: Weiming Hu, Jun Gao, Bing Li, Ou Wu, Junping Du, Stephen Maybank.
Machine Learning algorithms based on kernel density estimation (KDE) are said to be well suited for anomaly detection. However, existing approaches mainly ...
(2020) Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression. IEEE Transactions on Knowledge and Data Engineering, 32, 218 ...
Current local density-based anomaly detection methods are limited in that the local density estimation and the neighborhood density estimation are not accurate ...
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Machine Learning algorithms based on kernel density estimation (KDE) are said to be well suited for anomaly detection. However, existing approaches mainly cover ...
Jun Gao's 11 research works with 488 citations, including: Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression.