Loda: Lightweight on-line detector of anomalies

By Tomás Pevný. Machine-mediated learning, 2016

398 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Epidemiology

View Paper: https://www.semanticscholar.org/paper/ca56df64a351f873c8c138874326a6f64eec011d

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. Chunhua Shen
2. Anton van den Hengel
3. Dan Hendrycks
4. Thomas G. Dietterich
5. Xiyang Hu
6. Guansong Pang
7. Yue Zhao
8. Scott Purdy

In Computer Networks and Communications

1. Dan Hendrycks
2. Guansong Pang
3. Chunhua Shen
4. Scott Purdy
5. Zuha Agha
6. Thomas G. Dietterich
7. Alexander Lavin
8. Subutai Ahmad

In Epidemiology

1. Scott Purdy
2. Zuha Agha
3. Alexander Lavin
4. Subutai Ahmad
5. Thomas G. Dietterich
6. Alan Fern
7. Alessandro Rinaldo
8. Chunhua Shen

Citations in the corpus

• Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection (2019, methodology): “Both methods are shown to have competitive performance in various comparative studies [21, 22, 12, 23].”
• ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions (2022, background): “…Outlier Score (HBOS) [30], Isolation Forest (IForest) [17], k Nearest Neighbors (KNN) [15], Lightweight On-line Detector of Anomalies (LODA) [58], Local Outlier Factor(LOF) [34], Locally Selective Combination in Parallel Outlier Ensembles (LSCP) [18], One-Class Support Vector Machines…”
• Deep Learning for Anomaly Detection (2020, methodology): “w space to calculate anomaly scores. Compared to the dimension reduction methods that are popular in anomaly detection, such as principal component analysis (PCA) [21, 141, 180] and random projection [81, 114, 124], deep learning techniques have been demonstrating substantially better capability in ”
• Open Category Detection with PAC Guarantees (2018, result): “In the Supplementary Materials we show similar results with the LODA anomaly detector (Pevn ´ y, 2015).”
• Unsupervised real-time anomaly detection for streaming data (2017, background): “Additional techniques for general purpose anomaly detection on streaming data include [9,43,44].”
• Extended Isolation Forest (2018, background): “been developed over the years to address this need including multivariate data [15] and more recently, streaming data with need for updates on data with missing variables [14].”