Outlier Detection with Kernel Density Functions

By Longin Jan Latecki, Aleksandar Lazarevic, Dragoljub Pokrajac. IAPR International Conference on Machine Learning and Data Mining in Pattern Recognition, 2007

325 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/be49dbac8e395dba3e8f918924ffe4a55dec34ca

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Citations in the corpus

Robust kernel density estimation (2008, methodology): “larger where the data are less dense, with the aim of decreasing the aforementioned bias. Such methods have been applied in outlier detection and computer vision applications (Comaniciu et al., 2001; Latecki et al., 2007), and are one possible approach to robust nonparametric density estimation. We ”
Progress in Outlier Detection Techniques: A Survey (2019, methodology): “detection using kernel functions was presented in [108] by Latecki et al.”
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals (2021, methodology): “Specifically, RPCA is used to identify a low rank representation including random noise and outliers by using a convex relaxation of the rank operator; 2) Clustering analysis is used for anomaly detection, such as Gaussian Mixture Models (GMM) [30], k-means [18] and Kernel Density Estimator (KDE) [3”
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation (2020, background): “Ensembles of Gaussian Mixture Models [9], kernel density estimate [19]) iii) one-class classification (OCC): finding a separating manifold between normal data and the rest of input space (e.”
Sub-Image Anomaly Detection with Deep Pyramid Correspondences (2020, background): “Kernel density estimation [21] is a notable non-parametric method.”
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation (2024, unknown): “…including Mahalanobis distance (Maha) (Hardin and Rocke, 2004; Lee et al., 2018), One-class SVM (SVM) (Schölkopf et al., 2001), Local Outlier Factor (LOF) (Breunig et al., 2000), Kernel Density Estimation (KDE) (Latecki et al., 2007), and Copula-Based Outlier Detection (COPOD) (Li et al., 2020).”