Group Anomaly Detection using Flexible Genre Models

By Liang Xiong, Barnab'as P'oczos, Jeff Schneider. 2011

132 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

1. Krikamol Muandet
2. Bharath K. Sriperumbudur
3. Kenji Fukumizu
4. Bernhard Schölkopf
5. Arthur Gretton
6. Stéphane Canu
7. Jorge Guevara
8. Roberto Hirata Jr.

In Anomaly & Outlier Detection

1. Bharath K. Sriperumbudur
2. Jorge Guevara
3. Stéphane Canu
4. Roberto Hirata Jr.
5. Kenji Fukumizu
6. Bernhard Schölkopf
7. Krikamol Muandet
8. Thomas G. Dietterich

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).”
• A Unifying Review of Deep and Shallow Anomaly Detection (2020, background): “These include point anomalies, conditional or contextual anomalies [169], [171], [191]–[195], and group or collective anomalies [146], [193], [196]–[199].”
• Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019, methodology): “We compare the proposed method with previous state-of-the-art methods on the KDDCUP dataset, including OC-SVM [35], a deep clustering network (DCN) [42], DSEBM [43], DAGMM [48] and the baseline variants of MemAE.”
• 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].”
• Support Measure Data Description for group anomaly detection (2015, background): “Group anomalies can be given by [32]: 1) point-based anomalies, defined as being an aggregation of anomalous points; 2) distribution-based anomalies, defined as being an anomalous aggregation of non-anomalous points.”
• Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection (2018, methodology): “Unlike existing methods that only involve one of the aspects (Zimek et al. (2012); Zhai et al.”