Hierarchical Probabilistic Models for Group Anomaly Detection
By Liang Xiong, Barnab'as P'oczos, Jeff Schneider, Andrew J. Connolly, J. Vanderplas. 2011
96 citations recorded by the source corpus.
Subfields: Probabilistic & Bayesian Methods, Anomaly & Outlier Detection
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Probabilistic & Bayesian Methods
- Krikamol Muandet
- Bernhard Schölkopf
- Bharath K. Sriperumbudur
- Kenji Fukumizu
- Barnab'as P'oczos
- Arthur Gretton
- Stéphane Canu
- Jorge Guevara
In Anomaly & Outlier Detection
- Jorge Guevara
- Stéphane Canu
- Roberto Hirata Jr.
- Bernhard Schölkopf
- Krikamol Muandet
- Bharath K. Sriperumbudur
- Kenji Fukumizu
- Sungyong Seo
Citations in the corpus
- Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “…many classical problems such as multiple-instance learning (Doran 2013), learning from noisy data, and group anomaly detection (Póczos et al. 2011, Xiong et al. 2011b;a, Muandet and Schölkopf 2013), may be viewed as empirical risk minimization when training data are probability distributions…”
- One-Class Support Measure Machines for Group Anomaly Detection (2013, methodology): “The Flexible Genre Model (FGM) recently extends this idea to model more complex group structures (Xiong et al. 2011a).”
- Support Measure Data Description A One-Class Classifier for Group Anomaly Detection (2017, methodology): “State-of-the-art techniques for the group anomaly detection task are given by the hierarchical probabilistic model [30, 31] and the one-class support measure machine [19].”
- Group Anomaly Detection using Flexible Genre Models (2011, methodology): “The Mixture of Gaussian Mixture Model (MGMM) [13] firstly uses topic modeling for group anomaly detection.”
- Outlier Analysis (2013, background): “Numerous other applications of outlier analysis may be found in the domains of aviation safety [128], internet routing updates [373], malicious URL detection [319, 509], disease outbreaks [465], spatial linking of criminal incidents [311], failure management of large computer clusters [390], astrono”
- CSI: A Hybrid Deep Model for Fake News Detection (2017, background): “Such information is not feasible in practice, hence later works propose variants of mixtures models for the data, where the learned parameters are used to identify the anomalous groups [42, 43].”