Outlier Detection

By Yufeng Kou, Chang-Tien Lu. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2008

356 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8c40030e0385d50e4ed8a482fc0c601cbcf64317

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Arthur Zimek
2. Erich Schubert
3. Hans-Peter Kriegel
4. Pietro Michiardi
5. Maurizio Filippone
6. Jihane Zouaoui
7. Rémi Domingues
8. Abdun Naser Mahmood

In Civil and Structural Engineering

1. Nada Sahlab
2. Pietro Michiardi
3. Benjamin Maschler
4. Hans-Peter Kriegel
5. Jiankun Hu
6. Abdun Naser Mahmood
7. Michael Weyrich
8. Erich Schubert

In Control and Systems Engineering

1. Pietro Michiardi
2. Niloy J. Mitra
3. Jiankun Hu
4. Abdun Naser Mahmood
5. Maurizio Filippone
6. Gianfranco Genta
7. Raffaello Levi
8. Giulio Barbato

Citations in the corpus

• A survey on unsupervised outlier detection in high‐dimensional numerical data (2012, background): “[1–8], to name a few), the difficulties in high-dimensional data and specialized approaches in this area have not been sketched in any of those (though, notably, a recent textbook edition sketches three example approaches [9]).”
• Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “Several surveys reviewed methodologies on this topic, yet mostly within the field of data mining [16], [17], [18], [19].”
• PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds (2019, background): “…classical approaches for outlier detection, classification and removal have been proposed primarily in the statistics and data mining communities, in the general setting of point clouds in arbitrary dimensions, with several monographs dedicated specifically to this topic [Pin95,BG05,RH11,Agg15].”
• A Survey on Anomaly Detection for Technical Systems using LSTM Networks (2021, background): “Thus, probability densities have been calculated for target parameters and defined percentiles have been declared as outliers and thereby as anomalies of a certain degree [12].”
• A survey of network anomaly detection techniques (2016, methodology): “An anomaly can be categorized in the following ways (Ahmed et al., 2014, 2015a).”
• Features and performance of some outlier detection methods (2011, methodology): “A modified IQR method, conserving most attractive features and introducing sample size n into the picture, was proposed based on simple empirical considerations [4].”
• A comparative evaluation of outlier detection algorithms: Experiments and analyses (2018, methodology): “As such, the Mahalanobis distance is suitable for anomaly detection tasks targeting multivariate datasets composed of a single Gaussian-shaped cluster [2].”