LoOP: local outlier probabilities

By Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek. 2009

576 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Anomaly & Outlier Detection

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Citations in the corpus

Zero-Shot Learning Through Cross-Modal Transfer (2013, methodology): “An alternative would be to use the method of [17] to obtain an actual outlier probability in an unsupervised way.”
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild (2016, methodology): “They experiment with two novelty detection strategies: Gaussian and LoOP models [40].”
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey (2019, background): “On top of that, not to miss valuable information from clean hard samples, noisy data are weighted according to their noisiness level, estimated by pLOF [164].”
Iterative Learning with Open-set Noisy Labels (2018, background): “1As pointed by [17, 18], pcLOF is not sensitive to parameter k Figure 4.”
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data (2016, methodology): “From the 212 malignant instances, we kept the first 10 as anomalies (similar to [46]).”
An instance level analysis of data complexity (2014, background): “One approach is local outlier probability (LoOP) (Kriegel et al. 2009).”
A survey on unsupervised outlier detection in high‐dimensional numerical data (2012, methodology): “An improvement could be based on LoOP [138] or the general scaling and normalization methods discussed in ref.”
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection (2015, methodology): “LoOP [Kriegel et al. 2009a] uses a density estimation based on the distance distribution of all NNs and formulates the local outlier score as a probability.”
Progress in Outlier Detection Techniques: A Survey (2019, methodology): “Kriegel et al. [81], then formulated a more robust local density estimate for an outlier detection method called the Local Outlier Probabilities (LoOP) which combines the idea of providing an outlier ‘score’ with a probabilistic and statisticaloriented approach.”
Interpreting and Unifying Outlier Scores (2011, methodology): “While some methods provide probability estimates as outlier scores (e.g. [31]), for many methods, the scaling of occurring values of the outlier score even differs within the same method from data set to data set, i.e., outlier score x in one data set means, we have an outlier, in another data set…”
Bitcoin Mixing Detection Using Deep Autoencoder (2018, methodology): “Inspired by the success of Local Outlier Factor for graph anomaly detection, we use local outlier probabilities introduced in [14] to find the addresses transacting with the mixing service.”
Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “Local outlier factor (LOF) estimate the density of a given point via the ratio of the local reachability of its neighbors and itself [275], followed by derivations for robustification [276], [277] and simplification [278].”
Enhancing one-class support vector machines for unsupervised anomaly detection (2013, methodology): “The preprocessing of the breast-cancer dataset was identical to the one proposed in [15].”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “outlier probability models [157] are few famous examples of the nearest neighbor based techniques.”